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Video manipulation

Video manipulation denotes the deliberate alteration of digital video footage through editing, processing, or generative techniques to modify its content, sequence, or authenticity, ranging from rudimentary cuts and speed adjustments to advanced synthetic recreations.^[1]^[2] These methods exploit software tools and artificial intelligence algorithms, enabling changes such as splicing disparate clips to fabricate false narratives, overlaying fabricated elements, or generating entirely synthetic sequences that mimic real events with high fidelity.^[3]^[4] Historically rooted in analog film editing, the practice has accelerated with digital tools since the late 20th century, but the proliferation of accessible AI models since the mid-2010s has democratized sophisticated manipulations like deepfakes, which use machine learning to swap faces or voices seamlessly.^[5]^[6] While legitimate applications exist in film production and forensic analysis, video manipulation's defining controversies center on its weaponization for deception, including political disinformation campaigns that undermine elections, fabricated scandals eroding institutional trust, and non-consensual content exacerbating privacy violations and harassment.^[7]^[8]^[9] Empirical studies indicate that even detectable alterations can sway public opinion when contextually plausible, highlighting causal pathways from manipulated media to behavioral shifts like altered voting preferences or heightened skepticism toward authentic records.^[10]^[11]

Historical Development

Pre-Digital Techniques

Pre-digital techniques for video manipulation primarily drew from analog film practices, as electronic video emerged later and initially relied on similar optical and mechanical methods to alter or fabricate visual content. In the late 19th century, filmmakers like Georges Méliès pioneered in-camera effects such as the stop trick—achieved by halting the camera mid-shot, removing or adding elements, and resuming filming to create sudden appearances or disappearances—and multiple exposures, where film was rewound and exposed multiple times to superimpose images. These methods, used in Méliès's 1896 film The Vanishing Lady, enabled basic illusions without post-production equipment, relying on precise timing and physical intervention to manipulate perceived reality.^[12] Matte techniques advanced compositing capabilities, allowing separate elements to be combined seamlessly. Early glass matte shots, introduced by Norman Dawn in his 1907 short Missions of California, involved painting landscapes on glass placed in front of the camera lens, with the lower portion left clear to expose live action filmed against a black backdrop. By the 1920s and 1930s, optical printers facilitated traveling mattes, where a printer re-photographed footage through masks to isolate and layer subjects against new backgrounds, as seen in films like The Thief of Bagdad (1924). The Acme-Dunn optical printer, commercialized in the mid-1940s, standardized this process for complex multi-pass compositing, enabling manipulations like inserting actors into impossible environments without digital intervention.^[13]^[14] Other mechanical and optical methods included rear projection, which projected pre-filmed backgrounds onto a translucent screen behind actors, synchronizing motion to simulate dynamic settings, as employed in King Kong (1933) for jungle scenes. Miniatures and forced perspective created scale illusions, with detailed models filmed to mimic full-sized structures, often enhanced by matte work or controlled lighting to obscure seams. In the realm of early electronic video, post-World War II television adopted chroma keying, refined by Petro Vlahos's 1958 color separation process using blue-backings and filters to isolate foregrounds for live compositing, allowing real-time manipulations in broadcasts and pre-recorded tapes. These techniques, labor-intensive and prone to artifacts like halos or mismatched lighting, laid the groundwork for altering video content through physical and optical means rather than computational algorithms.^[12]^[12]

Transition to Digital Editing

The shift from analog to digital video editing marked a fundamental change in how footage could be manipulated, moving from linear, tape-based processes to non-linear, computer-mediated workflows that allowed non-destructive alterations and precise control. Analog editing, reliant on physical splicing of film or videotape, was inherently sequential and destructive, requiring editors to commit changes irreversibly and limiting revisions to the order of recorded material.^[15] This constrained complex manipulations, such as inserting effects or rearranging sequences without regenerating entire reels.^[16] Pioneering digital systems emerged in the mid-1980s, enabling the storage and processing of video data as binary files. In 1985, Quantel introduced the Harry system, the first non-linear digital video editor, which digitized footage for paintbox-style effects and basic compositing, allowing editors to apply transformations like keying and morphing without physical cuts.^[17] This hardware represented an early bridge to digital manipulation, though its high cost—over $1 million per unit—restricted it to high-end broadcast and effects facilities.^[18] The transition accelerated in 1989 with Avid Technology's release of the Avid/1 Media Composer, the first real-time digital non-linear editing system accessible to film professionals.^[19] Operating on Macintosh hardware, it ingested analog video via digitization, stored it on hard disks, and permitted random-access editing, effects layering, and audio synchronization at speeds viable for feature films.^[20] Its adoption in Hollywood, exemplified by its use in editing The Grifters (1990), demonstrated practical advantages: edits could be undone, timelines rearranged fluidly, and visual effects integrated seamlessly, reducing production times from weeks to days for certain sequences.^[15] By the early 1990s, declining hardware costs and formats like DV (introduced in 1995) further democratized digital workflows, enabling widespread frame-accurate alterations that foreshadowed advanced video manipulation techniques.^[21] This era's innovations causalized a surge in creative possibilities for video tampering, as digital representations decoupled footage from its physical substrate, permitting algorithmic interventions like color correction, object removal, and synthetic element insertion with minimal artifacts compared to analog optical printing.^[22] However, early systems' reliance on proprietary hardware and limited storage—often capping projects at minutes of footage—tempered immediate ubiquity, with full industry dominance not achieved until the late 1990s.^[23] Empirical evidence from production logs shows editing efficiency gains of up to 50% in digital suites versus analog by 1995, driven by iterative testing unbound by tape degradation or splice errors.^[24]

Rise of AI-Enabled Manipulation

AI-enabled video manipulation advanced significantly with the introduction of generative adversarial networks (GANs) in 2014, which enabled the synthesis of realistic images and laid the groundwork for video applications.^[25] These techniques were first applied to create convincing face swaps in videos around 2017, marking the practical rise of what became known as deepfakes.^[26] The term "deepfake" emerged in late 2017 when a Reddit user under the pseudonym "deepfakes" created a subreddit dedicated to sharing algorithms and videos featuring synthetic face manipulations, primarily non-consensual pornography involving celebrities.^[27] This platform facilitated the exchange of open-source code, accelerating accessibility and leading to over 90,000 members before its shutdown due to content violations. Early tools relied on consumer-grade hardware, building on prior research such as the 2016 Face2Face project, which demonstrated real-time facial reenactment. By 2018, the technology gained broader attention through non-pornographic demonstrations, including a viral video superimposing comedian Jordan Peele's face onto Barack Obama to illustrate manipulation risks, produced in collaboration with BuzzFeed.^[28] Open-source software like DeepFaceLab, released that year, democratized creation, allowing users to generate high-fidelity fakes with minimal expertise. Advancements continued rapidly; by 2019, improvements in GAN variants enabled more seamless video synthesis, reducing artifacts and supporting longer clips.^[29] Into the 2020s, integration of diffusion models and transformer architectures further enhanced realism and efficiency, enabling real-time manipulation and audio-visual synchronization.^[30] Mobile apps for deepfake generation appeared by 2020, such as Zao, which popularized short-form celebrity swaps in China before facing regulatory scrutiny.^[31] Detection challenges intensified as quality improved, with peer-reviewed studies noting human detection rates dropping to around 65% for sophisticated videos by 2023.^[32] This proliferation shifted video manipulation from specialized effects to ubiquitous tools, raising empirical concerns over verifiable media authenticity amid increasing computational accessibility.^[33]

Technical Methods

Conventional Video Editing

Conventional video editing encompasses the manual assembly and modification of video footage through cutting, sequencing, and applying basic effects, primarily using non-linear editing systems (NLEs) that allow random access to clips without sequential overwriting.^[15] This approach contrasts with linear editing, where changes required re-recording entire segments, and became feasible with early NLE hardware like the CMX 600, developed in 1971 by CMX Systems for television news editing. By 1989, Avid Technologies' Avid/1 system introduced digital non-linear workflows to film production, enabling editors to rearrange footage on a timeline interface without physical tape degradation.^[20] Core techniques include standard cuts for seamless scene transitions, jump cuts to condense time or create discontinuity, and match cuts linking disparate shots via visual or thematic similarity, such as the iconic bone-to-spaceship transition in 2001: A Space Odyssey (1968).^[34] J-cuts and L-cuts extend audio from one clip into the next (or vice versa), enhancing narrative flow by decoupling sound from visuals, while transitions like cross-dissolves or wipes provide smooth or stylized shifts between scenes.^[35] Additional manipulations involve trimming clips to alter pacing, color correction to adjust exposure and tone for mood or concealment, and basic compositing to overlay elements, all executed via software timelines in tools like Adobe Premiere Pro or Avid Media Composer.^[36] These methods facilitate video manipulation by enabling selective omission of context, such as excising portions of footage to misrepresent events, or rearranging sequences to imply false causal links, as seen in historical propaganda editing where spliced clips distorted political speeches.^[34] Audio adjustments, including dubbing or synchronization tweaks, can further deceive by fabricating dialogue or environmental cues, though limitations like visible seams in mismatched lighting or motion persist without advanced masking.^[37] Unlike AI-driven techniques, conventional editing demands skilled human intervention and source material proximity, restricting seamless face swaps or generative alterations but allowing verifiable traceability through edit logs in professional software.^[36]

Computer-Generated and Composited Effects

Computer-generated imagery (CGI) refers to synthetic visual content produced through algorithmic rendering of 2D or 3D models, enabling the creation of elements not physically present during filming, such as fantastical creatures or architectural structures.^[38] Compositing integrates these CGI assets with live-action footage or other layers via digital tools, matching lighting, shadows, and motion to achieve perceptual realism.^[39] These techniques, predating AI-driven methods, rely on manual artistic and technical processes to manipulate video sequences, often employed in visual effects (VFX) pipelines but adaptable for deceptive alterations like fabricating events or altering participant actions.^[40] Early digital CGI emerged in the late 1970s, with Industrial Light & Magic's work on Star Wars (1977) incorporating rudimentary computer-assisted animations and wireframe models for spacecraft sequences.^[39] By the 1980s, films like Tron (1982) demonstrated fuller CGI integration, rendering glowing digital environments composited over live actors using scan-line rendering techniques.^[41] Compositing software advanced with tools like the Quantel Mirage (1980s), which supported real-time digital manipulation, evolving into multilayered workflows by the 1990s, as seen in Jurassic Park (1993), where ILM composited 3D dinosaur models onto practical sets via motion capture and ray-tracing for realistic skin and muscle simulation.^[40] These non-AI methods required extensive frame-by-frame adjustments, contrasting with later generative models by emphasizing deterministic physics simulations over probabilistic outputs.^[38] Core techniques include 3D modeling (e.g., polygonal meshes or NURBS surfaces for object geometry), texturing (mapping surface details), and lighting/shading (simulating photon interactions via radiosity or global illumination algorithms) for CGI generation, followed by compositing steps like chroma keying to isolate subjects against uniform backgrounds (typically green or blue screens) and alpha matting to blend transparencies.^[42] Rotoscoping traces live elements for precise masks, while particle systems simulate dynamic effects like explosions or crowds through scripted behaviors.^[39] Professional software such as Autodesk Flame or Nuke facilitates node-based workflows for these operations, allowing operators to track camera motion, correct color grading discrepancies, and integrate renders with sub-pixel accuracy to minimize artifacts like edge halos.^[43] In video manipulation contexts, CGI and compositing enable the insertion of fabricated objects—such as weapons or vehicles—into authentic footage, as demonstrated in military simulations or hoax videos where rendered elements mimic real physics without on-site filming.^[40] For instance, pre-AI forgeries have composited actors' faces onto body doubles using morphing tools, or augmented crowd sizes by duplicating and animating replicated figures, requiring skilled calibration to avoid inconsistencies in parallax or occlusion that betray synthesis.^[44] Detection challenges arise from high-fidelity outputs, though manual methods often leave traces like inconsistent specular highlights or mismatched grain noise, verifiable through forensic analysis of frame metadata or lighting discrepancies.^[39] These techniques, while computationally intensive (e.g., rendering a single complex scene could take hours on 1990s hardware), provide controllable realism for both legitimate VFX and illicit alterations, underscoring the need for provenance tracking in digital media.^[41]

Deep Learning and Generative Models

Deep learning generative models, such as Generative Adversarial Networks (GANs) and diffusion models, enable sophisticated video manipulations by synthesizing or altering visual elements with high fidelity, often preserving temporal dynamics across frames. These models learn probabilistic distributions of video data, allowing for tasks like facial reenactment, where expressions from a source video are transferred to a target subject's appearance. GANs, introduced by Ian Goodfellow and colleagues in June 2014, form the cornerstone of early deepfake technologies through an adversarial training process where a generator creates synthetic frames and a discriminator evaluates their authenticity. In video applications, variants like conditional GANs facilitate face swapping by conditioning generation on source identity and target pose, typically involving preprocessing steps such as landmark detection and alignment to maintain consistency. Early implementations, popularized in 2017 via open-source tools on platforms like Reddit, relied on autoencoder architectures augmented with GAN losses to train on datasets of thousands of facial images per subject, producing manipulated celebrity videos that sparked widespread concern.^[45] Subsequent advancements incorporated recurrent neural networks or optical flow estimation to enforce temporal smoothness, reducing artifacts like flickering in manipulated sequences. For instance, techniques in papers from 2018 onward used StyleGAN architectures to generate high-resolution facial textures adaptable to video frames.^[46] Diffusion models, gaining prominence after 2020, have further elevated manipulation quality by iteratively denoising latent representations, enabling more coherent video edits such as attribute modification or full-scene synthesis from text prompts. Surveys note their integration into deepfake pipelines by 2023, offering superior detail over GANs but at higher computational cost.^[33] These models' efficacy stems from large-scale training on datasets like FFHQ or CelebA for faces, extended to video via frame interpolation, though challenges persist in generalizing to diverse lighting, angles, and ethnicities without overfitting. Peer-reviewed analyses highlight that while GAN-based deepfakes dominated until 2022, diffusion-based approaches now predominate in state-of-the-art manipulations due to reduced mode collapse and improved realism.^[47]

Beneficial Applications

Entertainment Industry Innovations

Video manipulation technologies, particularly those leveraging artificial intelligence and computer-generated imagery, have revolutionized production processes in the entertainment industry by enabling realistic de-aging of actors and the recreation of deceased performers. De-aging techniques first appeared in X-Men: The Last Stand (2006), where computer-generated effects digitally altered the appearances of Patrick Stewart and Ian McKellen to depict them as younger versions for a flashback sequence.^[48] Subsequent advancements allowed for more seamless applications, as seen in The Irishman (2019), where machine learning models processed facial data to de-age Robert De Niro, Al Pacino, and Joe Pesci across decades-spanning scenes.^[49] These methods reduce the need for extensive makeup or body doubles, streamlining workflows while preserving narrative continuity. Deepfake technology, which swaps faces using generative adversarial networks, has extended these capabilities to posthumous actor resurrections, allowing studios to insert digital likenesses into new footage. In Rogue One: A Star Wars Story (2016), Peter Cushing's likeness as Grand Moff Tarkin was recreated via CGI facial mapping onto actor Guy Henry's body, drawing from archival footage to mimic mannerisms.^[50] Similar techniques featured a de-aged Luke Skywalker in The Book of Boba Fett (2022), blending archival performance with AI-enhanced visuals for a brief appearance.^[51] Plans to cast a digital James Dean in Back to Eden (announced 2019, production ongoing as of 2023) highlight ongoing ethical debates, though such uses prioritize visual fidelity over consent from estates.^[50] Virtual production innovations, exemplified by LED wall arrays, integrate real-time video manipulation to project dynamic backgrounds directly onto sets, minimizing post-production compositing. Industrial Light & Magic's StageCraft system, debuted in The Mandalorian (2019), employed massive curved LED screens displaying game-engine-rendered environments, enabling accurate lighting reflections on actors and props during principal photography.^[52] This approach cut location scouting costs and accelerated editing timelines, with reflections and parallax effects providing naturalistic integration unattainable with traditional green screens.^[53] By 2021, the technology proliferated to other productions, fostering efficiency gains estimated at 50% in visual effects pipelines through reduced manual rotoscoping and keying.^[54] These tools underscore video manipulation's shift from corrective post-effects to proactive creative enablers, enhancing immersion while challenging conventional filming paradigms.

Educational and Professional Training

In medical training, synthetic video simulations employing AI-generated avatars and dialogues facilitate practice of patient interactions without real-world risks. For example, large language models such as ChatGPT 4o and Claude 3.5 Sonnet have been used to create virtual standardized patients (VSPs) for plastic surgery scenarios, including history-taking and shared decision-making, with evaluations in a 2025 study showing high realism and medical accuracy scores exceeding 4.5 out of 5 across ten cases assessed by clinical experts.^[55] Similarly, multimodal generative AI enables real-time video-based simulations of difficult conversations, such as in palliative care, using avatars that mimic diverse patient profiles in ethnic backgrounds, beliefs, and personalities, providing scalable, low-cost training for medical trainees.^[56] Universities like Coventry have implemented AI-generated avatars to simulate routine check-ups and rare emergencies, allowing students to rehearse diagnoses and procedures in interactive environments.^[57] In broader professional and vocational training, AI-generated videos support skill acquisition through customized instructional content. A 2024 experimental comparison found AI-produced teaching videos comparable to human-recorded ones in learner comprehension and engagement for procedural tasks, with advantages in production speed and adaptability.^[58] Synthetic video motion learning aids vocational fields like mechanics or crafts by overlaying instructional animations on real footage, enhancing motor skill replication as demonstrated in engineering education prototypes using slider-based manipulation for precise motion capture and replay.^[59] Educational applications leverage video manipulation for immersive content, though empirical implementations remain emerging. Potential uses include simulating historical events via deepfake recreations of figures, fostering deeper contextual understanding in classrooms, as explored in university curricula emphasizing virtual reality integrations.^[60] In corporate settings, these technologies enable personalized training modules, such as scenario-based videos for compliance or soft skills, reducing production costs while maintaining efficacy in knowledge retention.^[61] Overall, such methods prioritize controlled, repeatable exposure to complex scenarios, though their adoption requires validation against traditional media to ensure pedagogical equivalence.^[62]

Scientific and Forensic Utilities

In forensic investigations, digital video enhancement techniques are employed to clarify low-quality recordings from sources such as surveillance cameras, body-worn devices, or mobile phones, enabling identification of individuals, vehicles, or actions without fabricating new content.^[63] Common methods include sharpening to enhance edge definition, stabilization to reduce motion artifacts from shaky footage, and contrast adjustments to reveal details in shadowed or overexposed areas.^[64] These manipulations adhere to protocols ensuring chain-of-custody integrity and minimal alteration, as outlined in forensic best practices, to maintain admissibility in court.^[65] For instance, deinterlacing converts interlaced video fields into progressive frames, while frame averaging reduces noise by combining sequential frames, improving resolution for license plate recognition or facial feature extraction in cases like the 2013 Boston Marathon bombing analysis.^[66]^[67] Video manipulation also supports forensic reconstruction, where software composites multiple camera angles or simulates trajectories to model crime scenes, aiding in ballistics or accident reconstruction.^[68] Tools like resolution upscaling via super-resolution algorithms extrapolate pixel data based on patterns, recovering details from compressed CCTV footage without introducing artifacts beyond verifiable limits. The National Institute of Justice emphasizes that such enhancements, when documented with before-and-after comparisons, bolster evidentiary value in digital multimedia forensics.^[69] However, limitations persist; enhancements cannot restore information absent from the original recording, and overuse risks introducing perceptual biases, necessitating expert validation.^[70] In scientific research, video manipulation facilitates controlled experimentation by generating synthetic or altered footage to isolate variables, particularly in social sciences where deepfake technology creates realistic scenarios for studying human perception and behavior. A 2022 pilot study demonstrated that deepfakes enable ethical manipulation of speaker identities in videos to test biases, such as racial or gender stereotypes, yielding more precise causal inferences than traditional methods limited by real-world constraints.^[71] For example, researchers altered facial features or voices in interview clips to assess viewer trust, revealing measurable shifts in attribution of credibility without relying on actors or staging.^[71] This approach leverages generative models to produce high-fidelity stimuli, allowing replication and scalability across hypotheses on misinformation susceptibility or eyewitness reliability.^[71] Beyond social sciences, video editing tools aid in physical and biological simulations; for instance, compositing techniques overlay motion-captured data onto anatomical models to visualize surgical procedures or biomechanical stresses, as used in orthopedic research to predict implant failures under dynamic loads.^[72] In astronomy, time-lapse manipulations accelerate celestial events for analysis, though these prioritize raw data integrity over artistic alteration. Empirical validation remains essential, with studies cross-referencing manipulated outputs against ground-truth measurements to quantify accuracy, such as error rates below 5% in controlled kinematic reconstructions.^[72] These utilities underscore video manipulation's role in hypothesis testing, provided outputs are transparently documented to mitigate overinterpretation risks inherent in perceptual alterations.

Adverse Uses and Risks

Propagation of Misinformation

Manipulated videos propagate misinformation by creating deceptive visuals that mimic authentic footage, exploiting the persuasive power of moving images to fabricate events, statements, or behaviors. These alterations, from rudimentary edits to AI-generated deepfakes, spread rapidly on social media, where algorithmic amplification prioritizes engagement over veracity, often reaching millions before detection. Human accuracy in identifying high-quality video deepfakes averages 24.5 percent, allowing initial unchecked dissemination that sows confusion and reinforces biases.^[32]^[8] Early demonstrations underscored this risk. In April 2018, a deepfake video produced by Jordan Peele depicted Barack Obama delivering fabricated remarks voiced by Peele himself, viewed millions of times to illustrate technology's deceptive potential and warn against its misuse in disinformation.^[73] Similarly, a May 2019 manipulated clip of Nancy Pelosi, slowed to simulate slurred speech suggesting intoxication, garnered over 2.5 million views on Facebook, evading removal as it fell short of the platform's "manipulated media" threshold despite fact-checkers labeling it false.^[74]^[75] In electoral contexts, video manipulations have aimed to sway voters. During 2024 elections, deepfakes portrayed candidates uttering false endorsements or inflammatory comments, contributing to disinformation amid global polls, though fewer than 200 political deepfakes were documented in the U.S. with negligible proven vote influence compared to non-AI falsehoods.^[76]^[77] Synthetic videos, comprising a small fraction of content, disproportionately serve propaganda purposes but achieve lower virality than genuine material, limiting broad propagation yet enabling niche targeting.^[78] Such tactics extend to non-political arenas, fostering societal division. In October 2024, an AI-generated audio deepfake mimicking a Maryland school principal's racist remarks—paired with viral text claims of video evidence—sparked outrage, death threats, and community rifts before debunking, illustrating how even partial manipulations cascade into real-world harm.^[79] Overall, while potent for doubt induction, deepfakes' misinformation propagation hinges on pre-existing distrust, amplifying skepticism toward all audiovisual evidence rather than universally deceiving audiences.^[7]

Non-Consensual Exploitation

Non-consensual exploitation through video manipulation primarily entails the fabrication and dissemination of explicit content superimposing an individual's likeness—often via deepfake algorithms—onto pornographic footage without permission, functioning as a digital form of sexual abuse. This practice disproportionately targets women, with 99-100% of victims in deepfake pornography identified as female, and constitutes 96-98% of all online deepfake material as of 2025.^[32]^[80] Such content can be produced rapidly, requiring less than 25 minutes and minimal cost to generate a one-minute explicit video using freely available AI tools.^[81] High-profile instances underscore the scalability of this threat. In January 2024, explicit deepfake videos featuring singer Taylor Swift proliferated across platforms like X (formerly Twitter), amassing millions of views before removal, highlighting vulnerabilities even for public figures with robust security measures.^[82] Non-celebrity cases are more pervasive, including revenge porn where ex-partners or acquaintances manipulate existing videos or images; surveys indicate that 13% of U.S. teenagers in 2025 knew peers victimized by AI-generated deepfake pornography of minors.^[83] Child sexual abuse material (CSAM) exploitation has surged, with over 300 million children annually affected by online sexual abuse incorporating deepfakes, as reported by global analyses in 2024.^[84] A notable federal case involved a North Carolina psychiatrist sentenced to 40 years in prison in April 2024 for using generative AI to alter images of clothed children into explicit deepfakes.^[85] Victims endure profound psychological and social repercussions. Quantitative studies document elevated rates of depression, anxiety, and suicidal ideation among those subjected to non-consensual synthetic intimate imagery (NCII), with effects persisting due to the content's viral persistence online.^[86] Reputational damage compounds these harms, often resulting in employment loss, social isolation, and heightened vulnerability to further harassment or extortion (sextortion), where perpetrators demand compliance under threat of wider distribution.^[87] In educational settings, deepfake nudes have fueled cyberbullying, prompting school interventions but revealing gaps in institutional responses.^[88] The advent of accessible deepfake apps exacerbates this, enabling widespread abuse without technical expertise, as evidenced by rising detections of AI-generated CSAM by organizations like the Internet Watch Foundation.^[89] Beyond pornography, non-consensual manipulation extends to fabricated videos simulating harassment or defamation, such as altering footage to depict individuals in fabricated compromising acts for blackmail. Empirical data from 2024-2025 reports indicate that while pornographic deepfakes dominate, hybrid uses blending explicit and non-explicit elements amplify extortion risks, particularly in regions with lax platform moderation.^[6] Victims, predominantly women and minors, face causal chains of harm from initial creation to indefinite online availability, underscoring the technology's role in perpetuating gender-based violence without physical proximity.^[90]

Fraud and Economic Exploitation

Video manipulation technologies, particularly deepfakes, have enabled fraudsters to impersonate corporate executives and trusted figures in real-time video communications, facilitating unauthorized financial transactions. In one prominent case, a finance worker at a multinational company in Hong Kong authorized transfers totaling $25 million in February 2024 after participating in a video conference where fraudsters used deepfake technology to mimic the firm's chief financial officer and other colleagues, directing funds to fraudulent accounts.^[91] The scheme exploited the victim's reliance on visual cues for identity verification, bypassing traditional audio-only checks. Similar impersonation tactics have targeted businesses globally, with deepfake-enabled fraud contributing to over $200 million in losses during the first quarter of 2025 alone.^[92] Corporate wire transfer scams represent a core vector of economic exploitation, where manipulated videos create illusory consensus during high-stakes decisions. The U.S. Financial Crimes Enforcement Network (FinCEN) issued an alert on November 13, 2024, warning financial institutions of rising schemes involving deepfake media to target transfers, emphasizing the need for enhanced verification protocols beyond visual confirmation.^[93] A U.S. Securities and Exchange Commission statement from March 2025 noted that 92% of surveyed companies reported financial losses attributable to deepfakes, underscoring the technology's role in eroding internal controls.^[94] In July 2024, scammers attempted to defraud Ferrari using a deepfake audio impersonation of CEO Benedetto Vigna, though the attempt was thwarted, highlighting vulnerabilities in executive communications even among high-security firms.^[95] Beyond direct transfers, video manipulation facilitates investment and endorsement fraud, deceiving consumers into financial commitments. Fraudsters have deployed deepfakes of celebrities like Apple CEO Tim Cook to promote bogus cryptocurrency schemes, luring participants with fabricated endorsements that exploit brand trust for illicit gains.^[96] Deepfake fraud incidents in North America surged 1,740% from 2022 to 2023, driven by accessible generative AI tools that lower barriers for perpetrators targeting retail investors and businesses alike.^[92] These exploits not only drain individual and corporate assets but also undermine market stability, as seen in instances where manipulated videos of public figures trigger erroneous trading decisions.^[97]

Detection and Mitigation Strategies

Forensic and Manual Analysis

Manual analysis of manipulated videos relies on expert visual inspection to identify inconsistencies that automated systems might overlook, such as unnatural facial movements or environmental mismatches. Trained forensic examiners scrutinize elements like lighting discrepancies, where shadows or highlights fail to align with the scene's light sources, and reflection anomalies in the eyes that do not correspond to surrounding objects.^[98] Blending seams around the manipulated face, often visible as color shifts or edge blurring, provide additional cues, particularly in lower-quality forgeries.^[99] Biological signal examination forms a core component of manual forensic techniques, leveraging observable human physiological patterns absent or distorted in synthetic videos. Human subjects typically exhibit eye blinking rates of 15 to 20 times per minute, a frequency often reduced or irregularly patterned in deepfakes due to generative model limitations in simulating involuntary reflexes.^[100] Eye movement tracking reveals unnatural saccades or gaze fixation, while mouth and ear dynamics may show desynchronization from speech rhythms.^[101] Forensic analysis extends to physiological signal extraction, such as remote photoplethysmography (rPPG), which detects subtle skin color fluctuations indicative of heartbeat—typically 60 to 100 beats per minute in adults—from video pixel data. Deepfake videos frequently fail to replicate these periodic color variations accurately, as generative models prioritize visual fidelity over subsurface blood flow dynamics, enabling detection by amplifying and analyzing temporal signal consistency across facial regions.^[102] Heart rate estimation algorithms applied manually confirm discrepancies when compared to expected vital sign ranges, with studies showing detection accuracies exceeding 90% for certain datasets when biological signals are isolated.^[103] Metadata forensics, including compression artifacts and interframe inconsistencies, further corroborates findings by revealing editing traces like mismatched encoding parameters or duplicated frames.^[104]

Automated AI Detectors

Automated AI detectors for video manipulation utilize machine learning models, primarily deep neural networks, to classify content as authentic or synthetic by identifying subtle artifacts imperceptible to the human eye. These systems analyze features such as facial landmarks inconsistencies, unnatural blending at manipulation boundaries, temporal discontinuities in motion, and physiological signals like heartbeat-induced color fluctuations in skin pixels.^[105] Convolutional neural networks (CNNs) extract spatial features from frames, while recurrent neural networks (RNNs) or long short-term memory (LSTM) units process sequential data to detect anomalies in movement or lighting across time.^[106] Multimodal approaches integrate audio analysis, flagging desynchronizations between lip movements and speech or unnatural voice synthesis patterns.^[107] Prominent commercial tools include Reality Defender, which deploys ensemble AI models via API to scan videos for deepfake indicators, reporting detection rates exceeding 95% on standardized datasets like FaceForensics++.^[108] Deepware Scanner employs blockchain-verified scanning to pinpoint synthetic alterations, focusing on pixel-level anomalies and achieving high precision in controlled evaluations.^[109] McAfee's Deepfake Detector targets AI-generated audio in videos, alerting users within seconds by modeling vocal tract artifacts, with internal tests claiming over 90% accuracy for audio deepfakes.^[110] In a 2025 evaluation of commercial versus open-source detectors, tools like Bio-ID reached 98% accuracy on deepfake video benchmarks, outperforming open-source alternatives such as SBI by leveraging proprietary training data.^[111] Despite benchmark successes, real-world efficacy diminishes due to adversarial training in generators that evades detectors; studies indicate 45-50% accuracy drops against uncompressed, diverse deepfakes encountered online.^[32] For example, detectors trained on lab datasets falter on compressed social media videos or novel manipulation techniques, as evidenced by cross-dataset generalization tests showing false negative rates climbing to 30-40%.^[112] Peer-reviewed analyses emphasize the need for continual retraining, with hybrid models combining biological signal detection—such as micro-expressions or pupillary responses—yielding incremental improvements but remaining vulnerable to evolving threats.^[106] Deployment in platforms like content moderation systems thus often incorporates probabilistic scoring rather than binary verdicts to mitigate overconfidence.^[105]

Limitations and Evolving Challenges

Current detection strategies for video manipulation, encompassing both forensic analysis and automated AI systems, demonstrate limited generalization to novel generation techniques. Models trained on established datasets such as FaceForensics++ or Celeb-DF often achieve accuracies exceeding 90% in controlled evaluations but drop to below 60% when tested against unseen adversarial networks or distribution shifts in forgery methods. This vulnerability arises from over-reliance on dataset-specific artifacts, such as blending inconsistencies or frequency domain anomalies, which manipulators increasingly mitigate through iterative refinements in generative architectures.^[113] Automated detectors further contend with elevated error rates, including false positives and negatives that vary systematically across input variations like compression or lighting conditions. Empirical assessments reveal false positive rates climbing to 20-30% for compressed videos, undermining reliability in practical deployments such as social media moderation.^[114] Demographic biases exacerbate these issues, with studies documenting higher false positive rates—up to 15% greater—for faces of Black individuals compared to white counterparts in certain models, attributable to underrepresented training data rather than inherent algorithmic flaws.^[115] Manual forensic methods, while interpretable, remain labor-intensive and non-scalable, typically requiring hours per video and failing to address high-volume dissemination on platforms.^[116] Evolving challenges stem from the asymmetric arms race between manipulation creators and detectors, where generative models advance faster due to open-source proliferation and compute scaling. By mid-2025, techniques like diffusion-based synthesis have rendered many pre-2024 detectors obsolete, with cross-dataset generalization accuracies averaging under 70% against post-2023 forgeries. Adversarial evasion tactics, including targeted perturbations that exploit detector blind spots, further erode efficacy, as evidenced by success rates over 80% in fooling state-of-the-art systems in controlled benchmarks.^[5] Real-time detection lags critically, with processing latencies often exceeding seconds per frame, ill-suited for live streams or viral content propagation.^[117] Persistent dataset limitations compound these hurdles, as publicly available corpora lack diversity in ethnicities, ages, and forgery types, leading to overfitting and inflated in-sample performance metrics. High computational demands—frequently requiring GPU clusters for inference—restrict deployment to resource-constrained environments, while multimodal manipulations integrating audio-visual cues demand integrated frameworks that current siloed approaches inadequately address. These dynamics necessitate ongoing innovation, yet empirical evidence suggests detection trails generation by 6-12 months in capability cycles, perpetuating vulnerability to misinformation and exploitation.^[92]

Regulatory and Legal Responses

United States Policies

In October 2023, President Biden issued Executive Order 14110 on the safe, secure, and trustworthy development and use of artificial intelligence, directing federal agencies to establish standards for detecting and watermarking AI-generated content, including deepfake videos, to address risks such as election interference, fraud, and deception. The order mandated the National Institute of Standards and Technology (NIST) to develop guidelines for red-teaming AI models prone to generating synthetic media and required developers of advanced AI systems to report safety test results, with specific emphasis on mitigating deepfakes that could undermine public trust or national security.^[118] It also tasked the Department of Homeland Security with creating frameworks to counter AI-enabled disinformation campaigns involving manipulated videos.^[119] Following the 2024 election, President Trump in January 2025 rescinded portions of Biden's executive order deemed overly restrictive on innovation, while retaining elements focused on identifying synthetic content like deepfakes to protect against deception.^[120] This adjustment prioritized voluntary industry measures over mandatory regulations, reflecting concerns that heavy-handed rules could stifle AI advancement without empirically proven benefits in curbing misuse.^[120] In May 2025, President Trump signed the TAKE IT DOWN Act into law, establishing the first federal restrictions specifically targeting harmful deepfakes by prohibiting the distribution of non-consensual intimate videos or images generated or altered via AI, and requiring online platforms to implement removal mechanisms upon victim requests.^[121] The legislation imposes civil penalties for non-compliance and mandates platforms to develop reporting systems for such content, aiming to address exploitation without broader mandates on all manipulated media.^[122] It builds on existing federal laws like Section 230 but holds platforms accountable for rapid takedowns, though enforcement relies on user complaints rather than proactive monitoring.^[123] Proposed federal bills, such as the DEEPFAKES Accountability Act (H.R. 5586, introduced in 2023), seek to require watermarking and disclosure for AI-generated videos but remain pending as of October 2025, highlighting congressional divisions over balancing transparency with free speech and innovation.^[124] Similarly, the No AI FRAUD Act (H.R. 6943, introduced in 2024) aims to create civil remedies for unauthorized use of individuals' likenesses in deepfake videos, treating such manipulations as property rights violations, yet it has not advanced to enactment amid debates on its scope and First Amendment implications.^[125] These efforts underscore a patchwork approach, with federal policy emphasizing targeted harms like non-consensual exploitation over comprehensive bans on video manipulation, supplemented by state-level laws in over 40 jurisdictions addressing election-related deepfakes.^[126]

European and International Frameworks

The European Union's Artificial Intelligence Act (Regulation (EU) 2024/1689), which entered into force on August 1, 2024, establishes the world's first comprehensive legal framework for AI systems, including those enabling video manipulation such as deepfakes.^[127] Deepfakes are defined under Article 3(47) as "AI-generated or manipulated image, audio or video content that resembles existing persons, objects, places, entities or events" or alterations that appear authentic.^[128] Article 50 imposes transparency obligations on providers and deployers of AI systems generating or manipulating such content: outputs must be marked as artificially generated or manipulated in a detectable manner, unless the synthetic nature is apparent, the use serves artistic, satirical, or creative purposes, or it involves chatbots where disclosure suffices.^[128] Non-compliance can result in fines up to €35 million or 7% of global annual turnover, whichever is higher, enforced by national authorities and the European AI Office.^[129] The Act classifies deepfake-generating systems as high-risk if deployed in areas like biometric identification or critical infrastructure, requiring risk assessments, data governance, and human oversight, but prohibits them outright only if they enable practices like untargeted scraping of facial images for databases.^[130] Implementation phases roll out prohibitions immediately, general-purpose AI rules by August 2025, and full high-risk obligations by August 2027.^[131] Complementing the AI Act, the Digital Services Act (DSA), effective since November 2023 for large platforms, mandates online intermediaries to mitigate systemic risks from AI-generated videos, including dissemination of manipulated content posing threats to public security or civic discourse. Very large online platforms (VLOPs) with over 45 million users must conduct annual risk assessments for manipulative AI content, implement mitigation measures like content labeling or removal, and report to the European Commission; failure incurs fines up to 6% of global turnover.^[132] The DSA does not directly regulate deepfake creation but targets platforms' liability for hosting or amplifying unlabeled synthetic videos that qualify as illegal content, such as those inciting violence or fraud, emphasizing user notifications and appeal rights.^[133] Beyond the EU, European frameworks vary; the United Kingdom's Online Safety Act 2023, enacted May 2023, criminalizes sharing non-consensual intimate deepfake images with up to two years' imprisonment, focusing enforcement on platforms via Ofcom codes of practice for rapid removal.^[134] Internationally, no binding treaty specifically governs video manipulation, though soft-law instruments exist: UNESCO's 2021 Recommendation on the Ethics of Artificial Intelligence urges member states to address deepfakes through transparency, detection tools, and education to counter misinformation, without enforcement mechanisms. Discussions in forums like the UN's Ad Hoc Committee on cybercrime (2021–2024) and G7 Hiroshima AI Process (2023) highlight risks of AI-driven manipulation in elections and conflicts, promoting voluntary codes for watermarking and international cooperation, but lack mandatory provisions.^[135] The Council of Europe's 2024 Framework Convention on AI, open to non-members, emphasizes human rights safeguards against manipulative AI harms, requiring parties to assess and mitigate deepfake risks domestically. These efforts reflect nascent global coordination, prioritizing national implementation over unified enforcement amid concerns over jurisdictional gaps in cross-border content flows.^[136]

China and Authoritarian Approaches

In China, regulations on video manipulation, termed "deep synthesis," were formalized through the Provisions on the Administration of Deep Synthesis Internet Information Services, which took effect on January 10, 2023. These rules require service providers to obtain user consent for using their likeness in synthetic media, implement labeling mechanisms to mark altered content, and prevent the generation or dissemination of deepfakes that infringe on rights, fabricate facts, or disrupt social order.^[137]^[138] Providers must also conduct security assessments for algorithms capable of deep synthesis and retain records for traceability, with penalties including fines up to 100,000 yuan for violations.^[139] Complementing these, the Measures for the Labeling of AI-Generated Content, effective September 1, 2025, mandate explicit (e.g., watermarks or disclaimers) and implicit (e.g., embedded metadata) labeling for all AI-produced text, images, audio, and video distributed online via platforms like WeChat and Weibo.^[140]^[141] These measures aim to enhance content authenticity and curb misinformation, with platforms required to verify compliance and report non-adherence to authorities; non-compliance can result in content removal or service suspension.^[142] However, enforcement prioritizes state-approved narratives, as evidenced by the government's tolerance of AI tools for official propaganda while restricting private misuse. Despite these controls, Chinese state-linked actors have deployed deepfake videos for influence operations, including AI-generated anchors delivering scripted pro-Beijing messages on platforms like YouTube and Twitter in early 2023, mimicking Western news formats to amplify narratives on issues like Taiwan and COVID-19 origins.^[143]^[144] Such tactics, traced to operations like "Spamouflage," extend to foreign interference, as in a 2024 deepfake video undermining Philippine maritime claims against China.^[145] This state utilization underscores a selective application: regulations ostensibly protect public order but enable regime-aligned manipulation, aligning with broader authoritarian strategies to dominate information flows.^[146] Authoritarian regimes more generally leverage video manipulation for narrative control and repression, often inverting detection technologies for surveillance while dismissing authentic dissent footage as fabricated. In Russia, state media has amplified "deepfake" denials to discredit videos of military actions, eroding trust in visual evidence.^[147] Regimes like those in Iran and Venezuela employ AI-enhanced propaganda to fabricate endorsements or suppress opposition footage, prioritizing information dominance over transparency.^[148] These approaches reflect a causal dynamic where centralized power exploits technological asymmetries to manipulate public perception, with minimal accountability due to controlled media ecosystems.^[149] Empirical data from global assessments indicate rising digital repression tactics, including synthetic media, in at least 50 countries by 2023, correlating with governance models that prioritize regime stability over open discourse.^[150]

Critiques of Overregulation

Critics argue that regulatory efforts targeting video manipulation technologies, such as deepfakes, risk stifling innovation in artificial intelligence and media production by imposing burdensome compliance requirements on developers and users. Organizations like the Electronic Frontier Foundation (EFF) have cautioned against hasty legislation, noting that broad mandates could deter experimentation with synthetic media tools essential for advancements in entertainment, education, and journalism, without sufficient evidence of proportionate harm.^[151] Similarly, analyses from the Forbes contributor network emphasize that regulation should stem from demonstrated harms rather than speculative risks, as overbroad rules might suppress beneficial applications like visual effects in film or personalized learning content.^[152] A primary concern involves potential infringements on free speech protections, particularly under frameworks like the U.S. First Amendment, where satirical or parodic deepfakes—akin to political cartoons or comedy sketches—could be chilled by vague prohibitions on "deceptive" content. The Foundation for Individual Rights and Expression (FIRE) highlights that rushing to regulate deepfakes overlooks historical precedents where society adapted to disruptive technologies like photography or Photoshop without curtailing expression, advocating instead for non-legal countermeasures such as improved digital literacy and detection tools.^[153] The Cato Institute has criticized state-level deepfake bans, enacted in nearly one-third of U.S. states by 2024, for threatening core expressive freedoms by equating realism with illegality, potentially enabling selective enforcement against dissenting voices.^[154] The American Civil Liberties Union (ACLU) has litigated against such measures, arguing that individuals possess a constitutional right to create deepfakes absent direct harm like defamation, which existing tort laws already address.^[155] Enforcement challenges further undermine the efficacy of overregulation, as global dissemination of video manipulation tools renders unilateral policies toothless while inviting unintended consequences like underground development evading oversight. The New York Times reported in 2023 that deepfake laws often prove both overreaching—by mandating labels on benign content—and ineffective against malicious actors operating across borders, potentially diverting resources from targeted civil remedies.^[156] Federalist Society analyses warn that such frameworks could exacerbate problems by overregulating non-malicious synthetic media, fostering a chilling effect on creators without reducing actual incidents of fraud or misinformation, as evidenced by persistent deepfake proliferation despite early regulations in places like California.^[157] Proponents of restraint, including Brookings Institution scholars, stress that adaptive, evidence-based approaches—focusing on verifiable harms like election interference rather than blanket bans—better balance risks without compromising technological progress.^[158]

Broader Implications and Debates

Empirical Assessment of Threats

Empirical analyses reveal that video manipulation, particularly deepfakes, has seen exponential growth, with deepfake files surging from 500,000 in 2023 to an estimated 8 million in 2025, driven by accessible AI tools.^[80] Incidents rose 257% to 150 in 2024, with 179 reported in the first quarter of 2025 alone, predominantly involving non-consensual pornography targeting women and celebrities, who faced 47 instances in early 2025, an 81% increase from 2024.^[32]^[159] While potential harms are often highlighted in academic discourse, systematic reviews find limited evidence for broad societal threats like eroded public trust or systemic misinformation, with many claims relying on hypothetical scenarios rather than verified impacts.^[160]^[161] In financial fraud, deepfakes pose tangible risks, enabling scams via synthetic video and audio that have caused over $200 million in losses in the first quarter of 2025, contributing to total deepfake-related financial damages exceeding $1.56 billion for the year.^[162]^[163] These incidents, often in cryptocurrency and fintech sectors (accounting for 88% and 8% of cases respectively), exploit biometric impersonation for unauthorized transactions, with 49% of global businesses reporting audio-video deepfake fraud by 2024.^[164]^[165] Victims experience direct economic harm, though defenses like multi-factor authentication mitigate some risks; however, 77% of targeted individuals confirming losses highlight vulnerability in voice-cloned attacks.^[80] Non-consensual deepfake pornography represents the most prevalent misuse, functioning as image-based sexual abuse with severe psychological consequences for victims, including anxiety, depression, and reputational damage from viral dissemination.^[166]^[90] Studies document disproportionate targeting of women, with synthetic intimate imagery eroding privacy and enabling harassment, yet legal recourse remains inconsistent across jurisdictions.^[167] Empirical victim perspectives underscore long-term trauma akin to traditional revenge porn, amplified by the realism and scalability of AI generation.^[166] Regarding election interference, evidence of deepfake-driven influence remains scant despite warnings; analyses of recent elections, such as the 2024 U.S. cycle, conclude no widespread "deepfake election" occurred, with synthetic media failing to sway outcomes amid abundant genuine disinformation.^[168] Political deepfakes numbered 56 instances in early 2025, but systematic monitoring from 2020-2021 detected few high-impact cases altering voter behavior or trust.^[159]^[169] This gap between technological capability and empirical harm suggests overhyped threats in democratic processes, where legacy misinformation tools prove more effective than novel video manipulations.^[160] Broader assessments indicate deepfakes' threats are asymmetric and domain-specific, with fraud and personal abuse yielding measurable damages but lacking the predicted cascade into national security crises or truth erosion.^[170] Detection rates hover around 62% for human identification of images, underscoring ongoing challenges, yet 71% of organizations prioritize defenses, reflecting adaptive responses over panic.^[171]^[172] Credible sources, including peer-reviewed critiques, emphasize that while risks evolve, current data does not support claims of existential disruption without corresponding incidents.^[160]^[161]

Innovation Trade-Offs with Controls

Controls on video manipulation technologies, such as mandatory watermarking, disclosure requirements, and algorithmic detection mandates, introduce trade-offs by mitigating misuse risks while potentially increasing development costs and regulatory uncertainty for legitimate applications. Compliance with these measures often requires diverting engineering resources toward audit trails, transparency reporting, and risk assessments, which can elevate barriers to entry for smaller firms and slow iterative advancements in generative video tools used for film production, virtual reality training, and medical simulations. For instance, the European Union's AI Act, effective from August 2024, classifies general-purpose AI models capable of generating deepfake videos as high-risk systems, mandating detailed documentation and human oversight that proponents argue fosters accountability but critics contend burdens innovation with upfront costs estimated in the millions for model training and validation.^[131]^[173] In the United States, a patchwork of over 500 state-level AI bills introduced by 2025, including those targeting deepfake disclosures in elections and media, exacerbates these trade-offs by creating jurisdictional inconsistencies that complicate cross-state deployments of video AI software. Developers of tools like OpenAI's Sora, which generates realistic video sequences, face heightened liability risks under proposed federal frameworks, potentially discouraging experimentation with edge-case applications such as historical reenactments or personalized education content. Empirical analyses suggest that such fragmented regulation correlates with reduced venture capital inflows to AI startups, as investors prioritize compliant, low-risk paths over high-uncertainty breakthroughs in video synthesis.^[174]^[175] Authoritarian approaches, exemplified by China's 2023 deepfake regulations requiring real-time labeling and government pre-approval for synthetic media, illustrate extreme trade-offs where innovation in video manipulation is subordinated to content control, resulting in self-censorship among developers and a lag in domestic advancements compared to less regulated markets. While these controls prevent certain disinformation harms, they stifle dual-use technologies that could benefit sectors like entertainment exports or surveillance alternatives, with reports indicating slowed R&D in generative AI due to approval delays averaging six months. In contrast, lighter-touch policies in innovation hubs like Silicon Valley have accelerated video AI progress, though at the cost of sporadic misuse incidents that fuel calls for retroactive clamps.^[176] Detection-focused controls, such as embedded provenance standards proposed in frameworks like the EU AI Act's transparency obligations for deepfake generators, create an arms race dynamic where advancements in evasion techniques outpace safeguards, diverting talent from creative video applications to cat-and-mouse compliance engineering. Studies highlight that over-reliance on such controls can inadvertently suppress open-source contributions to video AI, as contributors avoid liability under vague "systemic risk" criteria applied to models exceeding computational thresholds of 10^25 FLOPs. Balancing these, evidence from pre-regulation periods shows unchecked innovation yielding tools with net positive utilities, suggesting that overly prescriptive controls risk broader societal costs if they prematurely constrain scalable video manipulation for non-malicious ends like accessibility aids for the hearing impaired.^[177]^[178]^[179]

Cultural and Societal Adaptations

The advent of accessible video manipulation technologies has induced a societal shift toward heightened skepticism regarding audiovisual evidence, with empirical surveys documenting a marked decline in public trust. A 2025 survey found that 85.4% of Americans reported reduced confidence in online news, photos, and videos over the preceding year, attributing this erosion directly to the realism of deepfakes.^[180] Experimental studies corroborate this, revealing that exposure to synthetic political videos fosters uncertainty rather than outright deception, thereby diminishing overall trust in social media news sources.^[181] In response, educational institutions and organizations have prioritized media literacy initiatives tailored to deepfake detection and critical evaluation. The Massachusetts Institute of Technology launched a dedicated learning module in 2021, updated through subsequent years, to impart skills for identifying misinformation amid AI-generated content.^[182] By 2025, programs expanded to include civics education toolkits, such as that from New York State United Teachers, which equip educators to teach concepts like misinformation propagation via deepfakes.^[183] These efforts emphasize not only technical detection but also fostering habits of source verification and contextual analysis, aiming to cultivate resilience against AI-mediated distortions.^[184] Culturally, video manipulation has spurred adaptations in creative and informational domains, including entertainment and journalism, where synthetic media prompts reevaluation of authenticity norms. Analyses indicate potential for deepfakes to enhance educational simulations while necessitating safeguards against misuse in news, with creators increasingly incorporating disclosure practices to maintain audience engagement.^[185] Societally, this has manifested in broader calls for interdisciplinary approaches, blending psychological insights with technological tools to mitigate interpersonal harms like memory alteration from immersive deepfakes.^[186] Such adaptations reflect a pragmatic recalibration, prioritizing empirical verification over presumptive credence in visual records.

References

[1]
Evaluating Online Information: Video Manipulation - Research Guides
Oct 1, 2025 · Common Video Manipulation Techniques · Editing: Cutting and splicing to change context or meaning. · Mislabeling: Using a video from one event to ...Missing: credible | Show results with:credible
[2]
Exposing Manipulated Photos and Videos in Digital Forensics ...
It is an artificial and automated manipulation of media, usually made by employing AI techniques, in which a person's face in an existing photo or video is ...
[3]
(PDF) Image and video manipulation: The generation of deepfakes
Apr 10, 2022 · In this study, we explain the different image, video, and audio manipulation techniques that are carried out with software and advanced audiovisual techniques.Missing: history | Show results with:history
[4]
The Dangers of Manipulated Media and Video: Deepfakes and More
Jun 6, 2023 · Tactics include reversing, deceptively editing or changing the speed of existing audio/visual media. Some synthetic media content may be created ...Missing: credible | Show results with:credible
[5]
DeepFake video detection: Insights into model generalisation
Mar 28, 2025 · The initial search setup was broad, incorporating combinations such as: (DeepFake OR FaceSwap OR Video manipulation OR Fake face/image/video) ...
[6]
[PDF] Increasing Threat of DeepFake Identities - Homeland Security
Whereas researchers have identified a number of weaknesses in image, video, and audio deepfakes as means of detecting them, deepfake text is not so easy to ...
[7]
Science & Tech Spotlight: Combating Deepfakes | U.S. GAO
Mar 11, 2024 · Malicious use of deepfakes could erode trust in elections, spread disinformation, undermine national security, and empower harassers. Key ...
[8]
Artificial intelligence, deepfakes, and the uncertain future of truth
Deepfakes can be used in ways that are highly disturbing. Candidates in a political campaign can be targeted by manipulated videos in which they appear to say ...
[9]
Dangers of Deepfake: What to Watch For - Stanford University
Feb 22, 2024 · Not only has this technology created confusion, skepticism, and the spread of misinformation, deepfakes also pose a threat to privacy and ...Missing: manipulation | Show results with:manipulation
[10]
The Video Manipulation Effect (VME): A quantification of the ...
The Video Manipulation Effect (VME): A quantification of the possible impact that the ordering of YouTube videos might have on opinions and voting preferences.
[11]
Political deepfake videos no more deceptive than other fake news ...
Aug 19, 2024 · New research from Washington University in St. Louis finds deepfakes can convince the American public of scandals that never occurred at ...
[12]
Special Effects in Film: A Brief History of Special Effects - MasterClass
Dec 22, 2021 · Special effects create movie illusions without using computer-generated imagery. Learn about different types of special effects and their long history here.
[13]
The History of VFX Matte Painting | MattePaint
Jul 29, 2022 · The Glass Shot was first used by director Norman Dawn (1884–1975) in the 1907 motion picture Missions of California. Dawn is credited with ...
[14]
The History of the Optical Printer - The Illusion Almanac
Mar 8, 2021 · Many histories of optical printing begin in the mid-1940s, when the Acme-Dunn optical printer hit the market as the first mass-produced device capable of doing ...
[15]
The Evolution Of Video Editing - Film Editing History - MASV
Nov 4, 2021 · Video editing has a rich history. We compiled the highlights over the decades, from scissors and tape to NLEs and AI.Missing: pre- manipulation
[16]
Inventing the non-linear edit suite by Chris Zwar - ProVideo Coalition
Jul 14, 2014 · Avid were the first non-linear editing system to offer reliable and integrated shared storage solutions, and to see the potential for large ...
[17]
The History and Evolution of Video Editing (From 1903 to Today)
Feb 15, 2025 · The history of video editing charts the evolution of storytelling from physical film splicing to today's AI-powered digital tools.Missing: pre- | Show results with:pre-
[18]
12.4 Quantel – Computer Graphics and Computer Animation
... Quantel introduced Harry, the world's first non-linear editor. NLEs became fairly standard in the editing business, making linear editing suites largely ...<|separator|>
[19]
Avid History - Going back in time
Jul 24, 2009 · NAB Show, Las Vegas, April 30, 1989 -- Avid Technology, Inc. today announced the Avid/1 TM Media Composer TM, a new real-time digital non-linear ...
[20]
An Observation in the History of Editing Software - IATSE Local 695
The practice of digital non-linear editing began in 1989, when Avid Technologies released the Avid/1, a turnkey, all-in-one editing platform.
[21]
Fun Facts and Dates in Digital Editing 'Firsts' - The Beat - PremiumBeat
Dec 10, 2011 · 1995: The DV codec and IEEE-1394 (FireWire 400) brought huge advancements to digital video recording, capturing and editing.
[22]
The Evolution of Video Editing | History of Film Editing
Apr 29, 2025 · This blog presents the evolution of video editing and core segments of the history of film editing from manual cuts to digital edits.
[23]
When and how the film business went digital - Stephen Follows
Jan 8, 2017 · The first tape-based editing suite was launched in 1971, but it wasn't until the 1980s that they become widely used. In the 1990s, computers had ...
[24]
The history and evolution of video editing software - Rashaad Sallie
Apr 17, 2024 · The introduction of digital technology and non-linear editing systems revolutionized the industry and made the process faster and more efficient ...
[25]
Deepfakes: How it all began - and where it could lead us
Apr 28, 2022 · AI-faked images and videos - so-called deepfakes - have developed rapidly in recent years. Here, we trace the history and describe the most important ...
[26]
Deepfakes, explained | MIT Sloan
Jul 21, 2020 · The term “deepfake” was first coined in late 2017 by a Reddit user of the same name. This user created a space on the online news and ...
[27]
A Brief History of Deepfakes - Reality Defender
The concept of deepfakes (or deepfaking) can be traced back to efforts starting in the 1990s, when researchers used CGI in attempts to create realistic images ...
[28]
'Inceptionism' and Balenciaga popes: a brief history of deepfakes
Apr 9, 2024 · What began as a primitive tool has spawned hyperrealistic fakes with the potential to disrupt elections around the world.
[29]
Deepfake Technology: The Frightening Evolution of Social ...
Deepfake technology has been available since at least 2017. Since then, we've seen rapid improvement in the technical quality of deepfakes and easier means to ...
[30]
(PDF) Ai-Driven Advances and Challenges in Deepfake Technology
Aug 9, 2025 · This paper offers a thorough introduction to deepfake technology, covering all of its types, including voice synthesis, gesture control, and face-swapping.<|control11|><|separator|>
[31]
The Emergence of Deepfake Technology: A Review | TIM Review
They were found through Google News search, using keywords “deepfake”, “deep fake”, and the corresponding plural forms. Once an article was found, a similar ...
[32]
Deepfake Statistics & Trends 2025 | Key Data & Insights - Keepnet
Sep 24, 2025 · Human detection rates for high-quality video deepfakes are 24.5%. · The market for AI detection tools is growing at a compound annual rate of ...Missing: advancements | Show results with:advancements
[33]
Generative Artificial Intelligence and the Evolving Challenge ... - MDPI
This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models.
[34]
What is Film Editing — Editing Principles & Techniques Explained
Feb 13, 2022 · Film editing is the craft of cutting and assembling finished film. In this post, we'll discuss the techniques and principles of editing ...
[35]
15 Creative Editing Techniques Every Video Editor Should Know
15 Creative Editing Techniques Every Video Editor Should Know · Standard Cut · Jump Cut · Montage · Cross Dissolve · Wipe · Fade In/Out · J or L Cut · Cutting on Action.
[36]
What Is Video Editing: Full Guide & Introduction - Riverside
Jul 30, 2024 · 6 Common video editing techniques · Cutting and trimming · Adding transitions · Applying effects · Audio adjustments · Color correction and grading.
[37]
10 Video Editing Techniques Every Editor Should Know - Artgrid
Dec 14, 2021 · 10 Video Editing Techniques Every Editor Should Know · Hard cut · J-cuts · L-cuts · Jump cuts · Smash cuts · Match cuts · Cutaways and inserts · Cutting ...
[38]
Filmmaking 101: What is CGI in Movies and Animation? - Boords
Nov 30, 2023 · In filmmaking and animation, CGI has evolved distinctly from traditional frame-by-frame animation, which involved hand-drawn illustrations, ...
[39]
Compositing - Everything You Need To Know - NFI
The 1970s and 1980s saw significant advancements in compositing technology with the introduction of computer-generated imagery (CGI). Films like “Star Wars” ( ...
[40]
(PDF) THE EVOLUTION OF VISUAL EFFECTS IN CINEMA
Nov 28, 2023 · This thorough study examines the development of visual effects (VFX) in movies from their beginning to the present, delving into the shift from practical ...
[41]
What is CGI? How Reality and CGI Blend in Films - PremiumBeat
Feb 2, 2024 · Journey through the history of CGI and take a look at how this art form may influence the future of filmmaking.
[42]
Green screen software - Chroma key video editing tools - Adobe
Luma key and chroma key effects found in Adobe Premiere Pro and After Effects, plus rotoscoping and other advanced compositing techniques in After Effects take ...<|separator|>
[43]
Compositing Software - Autodesk
VFX compositing software enables you to combine visual elements from separate sources, unifying them into a new visual product. The purpose of compositing ...
[44]
What Is Compositing? A Complete Guide to VFX and Video Editing
Jan 23, 2025 · History of Compositing. Compositing techniques date back to the early 20th century with Georges Méliès's works. He pioneered trick films and ...
[45]
Exploring deepfake technology: creation, consequences and ...
Sep 18, 2024 · This paper presents a comprehensive examination of deepfakes, exploring their creation, production and identification.<|separator|>
[46]
enochkan/awesome-gans-and-deepfakes - GitHub
A curated list of GAN & Deepfake papers and repositories. means implementation is available. GANs Tl;dr GANs containg two competing neural networks.
[47]
A systematic review of deepfake detection and generation ...
Oct 15, 2024 · In this study we explore automatic key detection and generation methods, frameworks, algorithms, and tools for identifying deepfakes (audio, images, and videos)
[48]
The History of Aging and De-Aging on Screen | Artlist
Jan 11, 2024 · Fast forward to 2006's X-Men: The Last Stand, where the first CGI de-aging effect was used. The opening scene of the movie shows 20 years ...
[49]
How De-aging Technology is Changing Hollywood & the Future of ...
Jan 27, 2021 · ... video production is deepfake. Deepfake is an ... visual effects (VFX) · deepfakes · AI-generated content · Respeecher for Business · Film ...
[50]
How AI is bringing film stars back from the dead - BBC
Jul 18, 2023 · The technology goes far beyond passive digital reconstruction or deepfake technology that overlays one person's face over someone else's body.
[51]
Creepy Uses of Deepfakes in Mainstream Movies and TV
Aug 11, 2024 · Creepy Uses of Deepfakes in Mainstream Movies and TV ; 5 The Book of Boba Fett ; 4 Dirty Pop: The Boy Band Scam ; 3 Deep Fake Love ; 2 Deep Fake ...
[52]
Art of LED wall virtual production, part one: lessons from ... - fxguide
Mar 4, 2020 · Compared to a traditional green screen stage, the LED walls provided the correct highlights, reflections, and pings on the Mandalorian's ...Missing: impact | Show results with:impact
[53]
This is the Way: How Innovative Technology Immersed Us in the ...
May 15, 2020 · Step inside the innovative technology developed for the Star Wars series, The Mandalorian, changing the future of filmmaking.Missing: impact | Show results with:impact
[54]
With LED Virtual Production Wall, Chapman Leaps Into the Future of ...
Apr 19, 2021 · A recently installed LED virtual production wall allows them to conjure digital settings for scenes as wild and diverse as their imaginations.
[55]
Synthetic Patient–Physician Conversations Simulated by Large ...
Jul 10, 2025 · Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large ...
[56]
Synthetic Patients: Simulating Difficult Conversations with Multimodal Generative AI for Medical Education
### Summary of Synthetic Patients for Simulating Difficult Conversations
[57]
https://www.digitalhealth.net/2024/04/coventry-uni-uses-ai-generated-avatars-to-train-medical-students/
[58]
Comparing human-made and AI-generated teaching videos
In the experiment, participants were assigned randomly into two conditions: the first condition presents the four videos in the sequence AI-generated video 1, ...
[59]
Developing vocational synthetic video motion learning using motor ...
Developing vocational synthetic video motion learning using motor slider. Tri Wrahatnolo, Setya Chendra Wibawa, Lilik Anifah, IGP Asto Buditjahjanto and ...
[60]
Deepfakes. - Berkeley Learning Platform
In education, it could enhance the learning experience by bringing historical figures to life or creating immersive virtual reality environments.
[61]
Enhance Training With AI-Generated Video Content
Aug 25, 2023 · This article explores how AI-generated video content has the potential to enhance training by offering tailored and engaging learning experiences.Missing: vocational | Show results with:vocational
[62]
From recorded to AI‐generated instructional videos: A comparison of ...
Oct 28, 2024 · In this study, we explored only one type of AI-generated video. Besides this B3 type, our pipeline can partially or fully generate other types ...Introduction · The Present Study And... · Methods<|control11|><|separator|>
[63]
15 Forensic Video Enhancement Techniques - SalvationDATA
Jun 21, 2022 · The 15 forensic video enhancement techniques list · 1. Sharpening · 2. Video stabilization · 3. Masking · 4. (De)interlacing · 5. Demultiplexing · 6.
[64]
Forensic Video Enhancement Services
Scaling and Zoom (upscales video resolution) · Sharpening · Stabilize (reduce video shaking) · Shadow and Highlight Adjustments (Exposure) · Frame Averaging · Speed ...
[65]
[PDF] Best Practice Manual for Forensic Image and Video Enhancement
This document addresses various types of issues concerning the forensic process for enhancement of digital image and video evidence from the scene of crime to ...
[66]
Forensic Video Enhancement
Forensic video enhancement is the process of clarifying digital images used as evidence. We enhance all forms of digital video including CCTV systems, ...
[67]
How Did Video Forensic Experts Reveal Hidden Evidence?
Apr 25, 2024 · Video forensic experts use specialized equipment and software to improve, analyze, and recover footage. These technologies help reveal the truth in video data.
[68]
Forensic Video Analysis & Enhancement: Unveil Hidden Details
Resolution Enhancement – Enhancing the resolution of a video is a critical aspect of forensic analysis, especially when dealing with low-quality footage.
[69]
Digital & Multimedia Evidence | National Institute of Justice
Computers are used for committing crime, and, thanks to the burgeoning science of digital evidence forensics, law enforcement now uses computers to fight crime.
[70]
[PDF] A Simplified Guide To Forensic Audio and Video Analysis
Video Enhancement Techniques -‐ A variety of enhancement techniques can be employed on video evidence. It is important that the best video recording be.
[71]
Using deepfakes for experiments in the social sciences - A pilot study
Nov 29, 2022 · By adapting this question to our experiment in order to prevent suspicion about a potential video manipulation, we shifted the focus from ...
[72]
Digital forensics approach for handling audio and video files
Video forensics plays a major role in understanding the crime situation and investigations [15]. Multimedia Forensics is used for the analysis of information ...
[73]
This PSA About Fake News From Barack Obama Is Not What It ...
Apr 17, 2018 · Posted on April 17, 2018, 3:26 pm. Sitting before the Stars and Stripes ... How To Spot A DeepFake Like The Barack Obama-Jordan Peele Video.
[74]
Manipulated Pelosi Video, Take 2 - FactCheck.org
May 31, 2019 · Another manipulated video of House Speaker Nancy Pelosi is circulating on Facebook. ... misinformation shared on social media. Our previous ...
[75]
Distorted Videos of Nancy Pelosi Spread on Facebook and Twitter ...
May 24, 2019 · “It's very subtle,” he said. “I think that is actually one of the most dangerous parts of disinformation and fake media.” The edited ...
[76]
How AI deepfakes polluted elections in 2024 - NPR
Dec 21, 2024 · How AI deepfakes polluted elections in 2024 · Untangling Disinformation · AI fakes raise election risks as lawmakers and tech companies scramble ...
[77]
Political Deepfakes and Elections | The First Amendment ...
Dec 6, 2024 · In 2024, there were fewer than 200 cases of political deepfakes reported and no criminal prosecutions. ... election misinformation remain ...
[78]
The spread of synthetic media on X - HKS Misinformation Review
Jun 3, 2024 · This study examines the prevalence and characteristics of synthetic media on social media platform X from December 2022 to September 2023.
[79]
The racist AI deepfake that fooled and divided a community - BBC
Oct 4, 2024 · When an audio clip appeared to show a local school principal making derogatory comments, it went viral online, sparked death threats against the ...
[80]
Deepfake Statistics 2025: AI Fraud Data & Trends - DeepStrike
Sep 8, 2025 · Deepfake files surged from 500K (2023) → 8M (2025). Fraud attempts spiked 3,000% in 2023, with 1,740% growth in North America.Missing: advancements 2017-2025
[81]
70 Deepfake Statistics You Need To Know (2024) - Spiralytics
Sep 26, 2024 · Deepfakes and pornography · Deepfake creators take less than 25 minutes and spend nothing to create a one-minute pornographic video of anyone—all ...
[82]
Taylor Swift and the Dangers of Deepfake Pornography
Feb 7, 2024 · We use "Deepfake pornography" as a term to describe a form of image-based sexual abuse. Not all forms of pornography are consensually made.
[83]
More Teens Than You Think Have Been 'Deepfake' Targets
Mar 3, 2025 · Thirteen percent of teenagers said they knew someone who had used AI to create or redistribute deepfake pornography of minors. These statistics ...
[84]
Over 300 million children a year are victims of online sexual ...
Over 300 million children a year are victims of technology-facilitated sexual exploitation and abuse ... deepfake technology – recently used to generate false ...
[85]
Charlotte Child Pornography Case Shows 'Unsettling' Reach of AI ...
Apr 29, 2024 · Child psychiatrist David Tatum was sentenced to 40 years in prison for using generative artificial intelligence to digitally alter clothed ...
[86]
The Mental Health and Social Implications of Nonconsensual ... - NIH
Five quantitative studies found evidence suggestive of increased depression, anxiety, and suicidal ideation in young people following NCSSI.
[87]
Survivor Safety: Deepfakes and the Negative Impacts of AI Technology
May 8, 2024 · Victims whose likenesses are exploited in deepfakes may face severe reputational harms, such as an inability to retain employment or having ...
[88]
AI 'Deepfakes': A Disturbing Trend in School Cyberbullying | NEA
Apr 10, 2025 · Mani's case has been the most high-profile, but deepfake images have ... definition of child abuse. “Schools and administrators and ...
[89]
How AI is being abused to create child sexual abuse material ...
The Internet Watch Foundation (IWF) has identified a significant and growing threat where AI technology is being exploited to produce child sexual abuse ...
[90]
When non-consensual intimate deepfakes go viral: The insufficiency ...
Jul 4, 2024 · However, the term 'deepfake porn' implies a form of erotic material, rather than a form of abuse, and risks conflating legal pornography with ...
[91]
Finance worker pays out $25 million after video call with deepfake ...
Feb 4, 2024 · A finance worker at a multinational firm was tricked into paying out $25 million to fraudsters using deepfake technology to pose as the company's chief ...
[92]
Detecting dangerous AI is essential in the deepfake era
Jul 7, 2025 · Detecting dangerous AI and deepfakes is not just a technical challenge, it's key to preserving public trust. The finance worker in Hong Kong ...
[93]
FinCEN Issues Alert on Fraud Schemes Involving Deepfake Media ...
Nov 13, 2024 · FinCEN issued an alert to help financial institutions identify fraud schemes associated with the use of deepfake media created with generative artificial ...
[94]
[PDF] AI, Deepfakes, and the Future of Financial Deception - SEC.gov
Mar 6, 2025 · 2 “92% of companies have experienced financial loss due to a deepfake.” CFO Magazine. November 6, 2024. 3 “Hong Kong Clerk Defrauded of $25 ...
[95]
How Ferrari Hit the Brakes on a Deepfake CEO
Jan 27, 2025 · How Ferrari Hit the Brakes on a Deepfake CEO. The luxury car manufacturer's close call with a deepfake scam surfaced lessons for all leaders.
[96]
The Deepfake Economy: A Critical Threat to Financial Leadership ...
Sep 10, 2025 · Deepfake-enabled fraud drove over $200 million in losses in Q1 2025 ... The scam lured thousands, exploiting Cook's credibility and Apple's brand ...
[97]
How a new wave of deepfake-driven cyber crime targets businesses
Deepfakes are a threat to markets and the economy at large: the deepfake of a Pentagon explosion caused panic on the stock market before officials could refute ...
[98]
Spotting tell-tale visual artifacts in face swapping videos - arXiv
Jun 19, 2025 · Video face manipulation detection through ensemble of CNNs. In International Conference on Pattern Recognition (ICPR), 2021. Available at ...
[99]
Deepfake video detection methods, approaches, and challenges
The research aims to enhance deepfake video detection by using advanced deep learning models and a new Particle Swarm Optimization (PSO) algorithm to improve ...<|separator|>
[100]
(PDF) DeepVision: Deepfakes Detection Using Human Eye Blinking ...
Apr 3, 2020 · In this paper, we propose a new approach to detect Deepfakes generated through the generative adversarial network (GANs) model via an algorithm ...
[101]
[PDF] deepfake detection using biological features - arXiv
Jan 14, 2023 · Broadly, we study five biological features: Eyebrow, eye blinking, eye movement, ear and mouth movement and heartbeat detection. 3 DEEPFAKE. 3.1 ...
[102]
High-quality deepfakes have a heart! - Frontiers
Apr 29, 2025 · In this study, we analyze deepfake videos of faces generated with novel methods regarding their heart-beat-related signals using remote photoplethysmography ( ...
[103]
DeepFakes Detection Based on Heart Rate Estimation: Single
Jan 31, 2022 · This chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate ...
[104]
a survey of digital forensic methods for multimodal deepfake ... - NIH
May 27, 2024 · This research tackles this knowledge gap by providing an up-to-date systematic survey of the digital forensic methods used to detect deepfakes.Missing: peer | Show results with:peer
[105]
Exploring autonomous methods for deepfake detection: A detailed ...
Feb 15, 2025 · This paper provides an in-depth analysis of state-of-the-art techniques and tools for identifying deepfakes, encompassing image, video, and audio-based content.
[106]
Advancements in detecting Deepfakes: AI algorithms and future ...
May 7, 2025 · Deepfakes detection is challenging due to their rapid generation and spread effectiveness. The research uses a dataset containing 5,000,000 ...
[107]
A review of deep learning based multimodal forgery detection for ...
Aug 25, 2025 · A review of deep learning based multimodal forgery detection for video and audio. Review; Open access; Published: 25 August 2025. Volume 7, ...
[108]
Deepfake Detection — Reality Defender
Reality Defender API and SDKs enable developers to use award-winning AI models to detect deepfake and AI-manipulated media. Get started for free with 50 ...
[109]
Deepware | Scan & Detect Deepfake Videos
Scan & Detect Deepfake Videos Scan a suspicious video to find out if it's synthetically manipulated.Contact Us for on-premise solutions.
[110]
McAfee® Deepfake Detector flags AI-generated audio within seconds
McAfee® Deepfake Detector uses advanced AI to alert you within seconds if a video has AI-generated audio, helping you quickly identify real vs. fake content ...
[111]
[PDF] Evaluating the Effectiveness of Deepfake Video Detection Tools
Commercial tools (Bio-ID, Deepware) performed better than open-source tools (SBI, LSDA, Lipinc) in detecting deepfake videos, with Bio-ID achieving 98% ...
[112]
What Journalists Should Know About Deepfake Detection in 2025
Mar 11, 2025 · These studies make one thing clear: deepfake detection tools cannot be trusted to reliably catch AI-generated or -manipulated content.
[113]
Improving Generalization in Deepfake Detection with Face ... - arXiv
Aug 27, 2025 · Despite recent advances, deepfake detection models often struggle to generalize beyond their training distributions, particularly when applied ...
[114]
https://ieeexplore.ieee.org/document/10002433
[115]
Study: New deepfake detector designed to less biased
Jan 16, 2024 · Deepfake detection algorithms often perform differently across races and genders, including a higher false positive rate on Black men than on white women.Missing: empirical | Show results with:empirical<|separator|>
[116]
https://ieeexplore.ieee.org/document/10245532
[117]
The Duality of AI and the Growing Challenge of Deepfake Detection
Jan 8, 2025 · The fight against deepfakes is an ongoing arms race. Here are three key areas for innovation: Real-Time Detection: Developing tools that ...
[118]
Biden Issues Executive Order to Create A.I. Safeguards
Oct 30, 2023 · The order also seeks to lessen the dangers of “deep fakes” that could swing elections or swindle consumers. “Deep fakes use A.I.-generated ...
[119]
Biden Signs Executive Order Regulating Artificial Intelligence
Oct 30, 2023 · Biden Signs Executive Order Regulating Artificial Intelligence ... deepfakes.” The Commerce Department is being asked to help with the ...<|separator|>
[120]
AI Tug-of-War: Trump Pulls Back Biden's AI Plans
Jan 25, 2025 · ... Order. Identifying and labeling synthetic content: The profusion of deepfakes and other deceptive AI-generated content has led to public ...
[121]
'Take It Down Act' Requires Online Platforms To Remove ...
Jun 10, 2025 · A new law makes it illegal to post unauthorized intimate images or deepfakes, and requires online platforms to (a) set up systems so victims ...Missing: video manipulation 2023-2025
[122]
AI and Deepfake Laws of 2025 - Regula Forensics
Aug 12, 2025 · However, in May 2025, the TAKE IT DOWN Act was signed into law, marking the first U.S. federal law directly restricting harmful deepfakes.
[123]
AI deepfakes in 2025: Global legal actions taken this year
Sep 16, 2025 · That changed in May 2025, when the TAKE IT DOWN Act was signed into law—the first federal legislation directly targeting harmful deepfakes.
[124]
DEEPFAKES Accountability Act 118th Congress (2023-2024)
Summary of H.R.5586 - 118th Congress (2023-2024): DEEPFAKES Accountability Act. ... Here are the steps for Status of Legislation: Introduced. Array ...
[125]
No AI FRAUD Act - Human Artistry Campaign
BMI applauds the bipartisan sponsors of the No AI FRAUD Act for recognizing the possible harm unauthorized AI deepfakes and clones can cause to songwriters, ...
[126]
Complete Guide to U.S. Deepfake Laws: 2025 State and Federal ...
Sep 2, 2025 · Federal TAKE IT DOWN Act signed by President Trump in May 2025 creates first federal framework; 47 states now have enacted deepfake legislation ...
[127]
AI Act | Shaping Europe's digital future - European Union
The AI Act (Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence) is the first-ever comprehensive legal framework on AI worldwide.European AI Office · Regulation - EU - 2024/1689 · AI Pact · AI Factories
[128]
Article 50: Transparency Obligations for Providers and Deployers of ...
AI systems that create synthetic content (like deepfakes) must mark their outputs as artificially generated. Companies must also inform users when they use AI ...
[129]
Deepfake, Deep Trouble: The European AI Act and the Fight Against ...
Nov 11, 2024 · Non-compliance with the mandated obligations is punishable by hefty fines that can reach up to 35 million euros or 7 percent of global annual ...
[130]
EU AI Act: first regulation on artificial intelligence | Topics
Feb 19, 2025 · Content that is either generated or modified with the help of AI - images, audio or video files (for example deepfakes) - need to be clearly ...
[131]
High-level summary of the AI Act | EU Artificial Intelligence Act
In this article we provide you with a high-level summary of the AI Act, selecting the parts which are most likely to be relevant to you regardless of who you ...
[132]
Risk in the Digital Services Act and AI Act: implications for media ...
May 27, 2025 · For instance, AI-generated news or deepfakes must clearly be labelled in order to avoid misleading the recipients. ... European Union's AI Act and ...<|control11|><|separator|>
[133]
Does the Digital Services Act achieve a balance between regulating ...
Jun 25, 2024 · This article examines the EU Digital Services Act (DSA) and its effectiveness in balancing protection against political deepfakes and freedom of expression.
[134]
Global governance challenges of deepfake technology - IAPP
Apr 23, 2025 · The U.K.'s Online Safety Act criminalizes the creation and distribution of nonconsensual sexually explicit deepfakes. Australia's Criminal Code ...
[135]
Regulating Deepfakes: Global Approaches to Combatting AI-Driven ...
Dec 11, 2024 · As deepfakes become more sophisticated and widespread, the urgency for effective regulation grows. Without consistent oversight, deepfakes risk ...
[136]
Emerging need to regulate deepfakes in international law
May 9, 2025 · Specifically, IHL regulatory frameworks must address the potential misuse of deepfakes in armed conflict. This could include explicit ...
[137]
China's deepfake regulation takes effect Jan. 10 - IAPP
China's deepfake regulation takes effect Jan. 10. Related stories. Notes from the Asia-Pacific region: China's AI Plus initiative accelerates AI integration.
[138]
Deep Synthesis Internet Information Services Regulations
Feb 1, 2023 · Under the Regulations, deep synthesis providers must, among other things: Prevent the use of deepfake services to produce, copy, publish or ...
[139]
China's Deep Fake Law Is Fake - AFCEA International
Jun 1, 2023 · The People's Republic of China issued the world's first deep fake regulation. The communist country sought to limit artificial intelligence creations.
[140]
China Releases New Labeling Requirements for AI-Generated ...
Mar 18, 2025 · The rules will take effect on September 1, 2025. The Labeling Rules impose explicit and implicit labeling obligations on “internet information ...
[141]
China's social media platforms rush to abide by AI-generated ...
Sep 1, 2025 · The law, which was issued in March, requires explicit and implicit labels for AI-generated text, images, audio, video and other virtual content.
[142]
China's New AI Labeling Rules: What Every China Business Needs ...
Sep 9, 2025 · The rules require that AI-generated content—text, images, audio, video, and virtual assets—be clearly identified when distributed on Chinese ...
[143]
The People Onscreen Are Fake. The Disinformation Is Real.
Feb 7, 2023 · Graphika said it discovered the deepfake videos while following social media accounts linked to a pro-China misinformation campaign known as “ ...
[144]
Deepfake news anchors spread Chinese propaganda on social media
Feb 9, 2023 · Deepfake news anchors spread Chinese propaganda on social media. In a series of videos posted on Twitter, Facebook and YouTube, Chinese state ...
[145]
China's high stakes and deepfakes in the Philippines - ASPI Strategist
Aug 2, 2024 · A covert social media campaign operated by the Chinese government appears to be spreading a deepfake video seeking to undermine support for Philippines ...
[146]
Deepfakes with Chinese Characteristics: PRC Influence Operations ...
Mar 29, 2024 · Second, AIGC could be used to strengthen PRC propaganda narrative, leveraging deepfake ... The use of deepfakes to “tell China's story well” ...
[147]
Authoritarian Regimes Could Exploit Cries of 'Deepfake' - WIRED
Feb 14, 2021 · Identifying doctored videos is essential. But assuming everything is faked allows autocrats to cast doubt on real videos of their violence.
[148]
[PDF] Digital Repression in Autocracies - V-Dem
Manipulating the information environment Beyond censorship, authoritarian regimes em- ploy a number of more proactive tactics to manipulate their information ...
[149]
The Rise of Digital Authoritarianism | Freedom House
This entails tackling social media manipulation and misuse of data in a manner that respects human rights, while preserving an internet that is global, free, ...
[150]
[PDF] ) Digital Repression Growing Globally, Threatening Freedoms
Apr 24, 2023 · Governments worldwide increasingly are blocking social media platforms, disrupting networks, manipulating online discussions, and arresting ...
[151]
Congress Should Not Rush to Regulate Deepfakes
Jun 24, 2019 · Before Congress drafts legislation to regulate deepfakes, lawmakers should carefully consider what types of content new laws should address.
[152]
The Case For Artificial Intelligence Regulation Is Surprisingly Weak
Apr 7, 2023 · The overall case for AI regulation is remarkably weak at the moment. First, regulation should be based on evidence of harm, rather than on the mere possibility ...
[153]
Deepfakes, democracy, and the perils of regulating new ... - FIRE
Oct 11, 2024 · The rush to regulate deepfakes and AI also overlooks the non-legal ways society can address the misuse of these technologies, including ...
[154]
Deepfake Crackdowns Threaten Free Speech - Cato Institute
Nov 22, 2024 · Deepfake fears have sparked rare bipartisan action, with nearly one-third of states passing laws to regulate their use during elections. Most ...
[155]
The ACLU Fights for Your Constitutional Right to Make Deepfakes
Jul 24, 2024 · The heart of the argument: Americans have a constitutional right to deepfake their fellow citizens. “Anytime you see large waves of bills ...
[156]
As Deepfakes Flourish, Countries Struggle With Response
Jan 22, 2023 · Deepfake laws, they said, risk being both overreaching but also toothless. Forcing labels or disclaimers onto deepfakes designed as valid ...
[157]
Deepfake Laws Risk Creating More Problems Than They Solve
Mar 1, 2021 · Deepfake pornography can cause victims stress, anxiety, and incur significant social cost. After Indian journalist Rana Ayyub criticized the ...
[158]
The three challenges of AI regulation - Brookings Institution
Jun 15, 2023 · Harms such as the violation of personal privacy, expansion of non-competitive markets, manipulation of individuals, and dissemination of hate, ...
[159]
https://surfshark.com/research/study/deepfake-statistics
[160]
Can deepfakes manipulate us? Assessing the evidence via a critical ...
May 2, 2025 · In general, we conclude that there are a great many papers decrying the risks and harms of deepfakes without the necessary empirical evidence to ...
[161]
What we know and don't know about deepfakes - Sage Journals
May 22, 2024 · A growing research field discusses deepfakes' potential harm (see, for example, Chesney and Citron, 2019a for an overview), however, much less ...
[162]
Deepfake-enabled fraud caused more than $200 million in losses
Apr 22, 2025 · In Q1 2025, deepfake-driven fraud led to $200 million in financial losses. A report from Resemble AI analyzes the economic consequences of deepfake attacks.
[163]
https://surfshark.com/research/chart/ai-deepfake-losses
[164]
2024 Deepfakes Guide and Statistics - Security.org
Aug 26, 2025 · Deepfakes have legitimate uses, but bad actors exploit them for purposes such as disinformation, blackmail, harassment and financial fraud. For ...
[165]
Deepfake Statistics 2025: The Hidden Cyber Threat - SQ Magazine
Oct 8, 2025 · 49% of businesses globally reported audio and video deepfake fraud incidents by 2024. Consumers lost $27.2 billion in 2024 to identity fraud, a ...
[166]
Sexualized Deepfake Abuse: Perpetrator and Victim Perspectives ...
Sep 9, 2025 · Advances in digital technologies provide new opportunities for harm, including sexualized deepfake abuse—the non-consensual creation, ...
[167]
Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes ...
Non-consensual deepfake pornography can be considered a form of image-based sexual abuse because intimate images are created and/or shared without the consent ...
[168]
Don't Panic (Yet): Assessing the Evidence and Discourse Around ...
Jul 7, 2025 · ... election, concluded that “... in the end this wasn't a deepfake election. … The warnings about AI were a distraction from the lack of clear ...
[169]
Deepfakes and Democracy (Theory): How Synthetic Audio-Visual ...
Based on a literature and media analysis, I systematize different types of deepfakes serving either disinformation or hate speech and outline how they weaken ...
[170]
Risks and benefits of artificial intelligence deepfakes - Elsevier
Aug 23, 2025 · In parallel, research focusing on hybrid warfare and diplomacy points out the asymmetric risks posed by deepfakes, highlighting how adversaries ...
[171]
Deepfake statistics (2025): 25 new facts for CFOs | Eftsure US
May 29, 2025 · 1 in 4 leaders unfamiliar with deepfakes · 31% underestimate deepfake fraud risk · 32% doubt employee ability to detect deepfakes · Over 50% lack ...
[172]
[PDF] Beyond Detection: The $280K Reality of Deepfake Attacks - Ironscales
Oct 7, 2025 · An overwhelming 71% of respondents say deepfake defense will be a top priority during that time (up from just 43% in 2024). • And yet, only 37% ...
[173]
Navigating Generative AI Under the European Union's Artificial ...
Oct 2, 2024 · This blog post focuses on how the EU's Artificial Intelligence Act (AI Act) regulates generative AI, which the AI Act refers to as General-Purpose AI (GPAI) ...
[174]
Regulating AI Without Strangling Innovation | IE Insights
Sep 3, 2025 · In 2025, over 500 AI-related bills were introduced across 42 states, addressing concerns around facial recognition, deepfakes, hiring algorithms ...
[175]
https://time.com/7327031/openai-sora-deepfakes-privacy/
[176]
[PDF] Regulating Deepfakes - Global Approaches to Combatting AI-Driven ...
Dec 11, 2024 · The growing threat of deepfakes underscores the urgent need for both technological and legal solutions to mitigate their harmful effects.
[177]
AI-driven disinformation: policy recommendations for democratic ...
Jul 31, 2025 · Crucially, deepfakes are transitioning from niche (mostly pornographic content) to mainstream weaponization in scams, politics, and malign ...
[178]
Deepfake detection in generative AI: A legal framework proposal to ...
The global deepfake threat: Challenges and the regulatory imperative. The alarming potential of deepfakes was starkly demonstrated in 2024 when a CEO was ...
[179]
The European Union AI Act: premature or precocious regulation?
Mar 7, 2024 · The Act is essentially a product safety regulation designed to reduce risks for humans from the use of AI systems.
[180]
85 % of Americans say deepfakes have eroded their trust in online ...
Jul 1, 2025 · 85.4 % report they have become less likely to trust news, photos or videos online in the past 12 months due to realistic deepfakes. Personal ...
[181]
Exploring the Impact of Synthetic Political Video on Deception ...
Feb 19, 2020 · We conclude that deepfakes may contribute toward generalized indeterminacy and cynicism, further intensifying recent challenges to online civic ...
[182]
Media Literacy in the Age of Deepfakes - MIT
This learning module aims to equip students with the critical skills to better understand the threat of misinformation.
[183]
https://www.nysut.org/news/2025/october/civics-education
[184]
Deepfakes and the crisis of knowing - UNESCO
Oct 1, 2025 · As deepfakes blur reality, education must go beyond detection, teaching students to navigate truth, knowledge, and AI-mediated uncertainty.
[185]
The potential effects of deepfakes on news media and entertainment
Oct 23, 2024 · ... propaganda and misinformation a new unpleasant strength (Tahir and Batool 2021). ... Deepfakes creating fake news where the authenticity of ...
[186]
The Social Impact of Deepfakes - Mary Ann Liebert, Inc.
Mar 17, 2021 · It is time to understand the possible effects deepfakes might have on people, and how psychological and media theories apply.