Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security
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Mirsky and Lee focused on reenactment approaches and replacement approaches.
“Mirsky and Lee [19] focused on reen- actment approaches (i.e., to change a target's expres- sion, mouth, pose, gaze or body), and replacement approaches (i.e., to replace a target's face by swap or transfer methods).”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [19]
Deepfakes threaten digital content authenticity, facilitating misinformation and manipulation.
“The rise of deepfakes-synthetic media generated using artificial intelligence-threatens digital content authenticity, facilitating misinformation and manipulation.”
From Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial Attacks (2025) · cites this paper as [2]
Deepfakes have been used to create fake news, commit fraud, and even produce explicit content without consent.
“Deepfakes have been used to create fake news, commit fraud, and even produce explicit content without consent (Chesney & Citron, 2019).”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Chesney & Citron, 2019
Synthetic media can be used to intimidate, threaten or harass political figures to influence their actions and statements, or deter political participation.
“Beyond disinformation campaigns aimed directly at undermining the credibility of candidates or influencing voter sentiments on specific issues, synthetic media can also be used to intimidate, threaten or otherwise harass political figures to influence their actions and statements, or to deter political participation altogether (Chesney & Citron, 2018).”
From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Chesney & Citron, 2018
Moderation and takedown mechanisms tend to lag behind the speed at which such content spreads, allowing politically motivated synthetic media to reach wide audiences before factcheckers can intervene.
“Consequently, moderation and takedown mechanisms tend to lag behind the speed at which such content spreads, allowing politically motivated synthetic media to reach wide audiences before factcheckers can intervene (Chesney & Citron, 2018).”
From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Chesney & Citron, 2018
The liar's dividend means that someone caught in wrongdoing can claim the incriminating video is merely synthetic.
“This forementioned liar's dividend means that someone caught in wrongdoing can claim the incriminating video is merely synthetic (Chesney & Citron, 2018).”
From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Chesney & Citron, 2018
With minimal technical expertise, perpetrators can fabricate highly realistic sexual content using another person's likeness, enabling sextortion, reputational sabotage, blackmail, and intimate partner violence.
“With minimal technical expertise, perpetrators can now fabricate highly realistic sexual content using another person's likeness, thereby enabling a continuum of exploitative practices that includes sextortion, reputational sabotage, blackmail, and intimate partner violence (Chesney & Citron, 2018).”
From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Chesney & Citron, 2018
Deepfake pornography targeting women in public life inflicts enduring reputational damage that cannot be easily corrected once the falsified content circulates.
“Comparable to revenge porn cases, where intimate images are shared without consent, or the proliferation of deepfake pornography targeting women in public life, these manipulations inflict enduring reputational damage that cannot be easily corrected once the falsified content circulates (Chesney & Citron, 2018).”
From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Chesney & Citron, 2018
Neither the malicious use of AI literature nor the synthetic media literature has fully addressed insider threat applications.
“Neither literature has fully addressed insider threat applications,the manufactured insider category is offered as one such application.”
From Geopolitical competition, online radicalisation, and organisational culture as vectors of the evolving insider threat (2026) · cites this paper as [8]
The manufactured insider category sits at the intersection of two trajectories anticipated in the academic literature: Brundage and colleagues' analysis of the malicious use of artificial intelligence and Chesney and Citron's analysis of synthetic media as a vector for deception in security-relevant contexts.
“The category sits at the intersection of two trajectories anticipated in the academic literature: Brundage and colleagues' analysis of the malicious use of artificial intelligence [4] and Chesney and Citron's analysis of synthetic media as a vector for deception in security-relevant contexts [8].”
From Geopolitical competition, online radicalisation, and organisational culture as vectors of the evolving insider threat (2026) · cites this paper as [8]
AI is a form of automation, but the term 'automation' more broadly encompasses the technique of making an apparatus, a process, or a system operate automatically.
“AI is a form of automation, but the term 'automation' more broadly encompasses 'the technique of making an apparatus, a process, or a system operate automatically.'”
From Artificial Intelligence and Privacy (2024) · cites this paper as [37]
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“…exposure to misinformation can be harmful and can have negative consequences for democratic governance as well as trust in news media and journalism more broadly (e.g., Bennett & Livingston, 2018; Chesney & Citron, 2018; Nisbet et al., 2021; Ognyanova et al., 2020; Vaccari & Chadwick, 2020).”
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BackgroundUsing Blockchain to Rein in the New Post-Truth World and Check the Spread of Fake News
Qayyum, A., Qadir, J., Janjua, M. U., & Sher, F. (2019) · IT Professional · doi:10.1109/mitp.2019.2910503 · Cited by 125 · see citing works
“Deep fakes and related technologies thus portend a looming challenge for privacy, democracy, national security, and ethical norms [3] and there is an urgent need to do research on the technical, legal, and ethical challenges that await the development of this field.”
BackgroundInfluentialDeep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, M. C., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. (2020) · Figshare · doi:10.6084/m9.figshare.12731039.v1 · Cited by 122 · see citing works
Flynn, A., Powell, A., Scott, A. J., & Cama, E. (2022) · The British Journal of Criminology · doi:10.1093/bjc/azab111 · Cited by 120 · see citing works
Deepfakes: perspectives on the future “reality” of advertising and branding
Kietzmann, J., Mills, A. J., & Plangger, K. A. (2020) · International Journal of Advertising · doi:10.1080/02650487.2020.1834211 · Cited by 114 · see citing works
“The “ Liar ’ s Dividend ” (Chesney and Citron 2019, 1753), for example, suggests that exposing content as fake or synthetic can actually have the adverse effect of stoking belief in the fakery.”
BackgroundPrivacy-Enhancing Technology and Everyday Augmented Reality
O’Hagan, J., Saeghe, P., Gugenheimer, J., Medeiros, D., Marky, K., Khamis, M., & McGill, M. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3569501 · Cited by 103 · see citing works
Advancing AI in healthcare: A comprehensive review of best practices
Polevikov, S. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117519 · Cited by 112 · see citing works
Testing human ability to detect ‘deepfake’ images of human faces
Bray, S. D., Johnson, S. D., & Kleinberg, B. (2023) · Journal of Cybersecurity · doi:10.1093/cybsec/tyad011 · Cited by 96 · see citing works
What Should ChatGPT Mean for Bioethics?
Cohen, I. G. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2233357 · Cited by 95 · see citing works
“…“ to insert faces and voices into video and audio recordings of actual people and enable the creation of realistic impersonations out of digital whole cloth ” (Chesney and Citron 2019), but some use it more broadly to mean the use of AI to create documentation that can fool most individuals.”
BackgroundAhmed, S. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101508 · Cited by 105 · see citing works
“Still, scholars believe that there is a cause of alarm since deepfakes now have demonstrated applicability in sabotaging elections and influencing civic trust (Chesney and Citron, 2019).”
BackgroundInfluentialThe detection of political deepfakes
Appel, M., & Prietzel, F. (2022) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmac008 · Cited by 95 · see citing works
“Study 3 Deepfakes in the political realm could be particularly harmful in combination with fake news stories (Chesney & Citron, 2019).”
BackgroundCreating, Using, Misusing, and Detecting Deep Fakes
Farid, H. (2022) · Journal of Online Trust and Safety · doi:10.54501/jots.v1i4.56 · Cited by 92 · see citing works
Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications
AL-KHAZRAJI, S. H., SALEH, H. H., KHALID, A. I., & MISHKHAL, I. A. (2023) · The Eurasia Proceedings of Science Technology Engineering and Mathematics · doi:10.55549/epstem.1371792 · Cited by 98 · see citing works
“(Chesney & Citron, 2019): Deepfakes enable the creation of compelling narratives that manipulate public opinion.”
Distinguishing Reality from AI: Approaches for Detecting Synthetic Content
Ghiurău, D., & Popescu, D. E. (2024) · Computers · doi:10.3390/computers14010001 · Cited by 108 · see citing works
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, M., Sankaranarayanan, A., Singh, N., Kim, D. Y., Lippman, A., & Picard, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51998-z · Cited by 89 · see citing works
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Triantafyllopoulos, A., Schuller, B. W., İymen, G., Sezgin, T. M., He, X., Yang, Z., Tzirakis, P., Liu, S., Mertes, S., André, E., et al. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3250266 · Cited by 89 · see citing works
“The most poignant of those issues is the rise of “deep fakes” (AI-fabricated videos of people saying or doing something that they have never said or done in real life) [181].”
BackgroundReading out of context: pornographic deepfakes, celebrity and intimacy
Popova, M. (2019) · Porn Studies · doi:10.1080/23268743.2019.1675090 · Cited by 87 · see citing works
Арсеньевич, П. К. (2020) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-35746-7_3 · Cited by 86 · see citing works
Twomey, J., Ching, D., Aylett, M. P., Quayle, M., Linehan, C., & Murphy, G. (2023) · PLOS ONE · doi:10.1371/journal.pone.0291668 · Cited by 87 · see citing works
Whittaker, L., Mulcahy, R. F., Letheren, K., Kietzmann, J., & Russell‐Bennett, R. (2023) · Technovation · doi:10.1016/j.technovation.2023.102784 · Cited by 86 · see citing works
Lai, X., & Rau, P. P. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106894 · Cited by 85 · see citing works
A Review of Image Processing Techniques for Deepfakes
Shahzad, H. F., Rustam, F., Flores, E. S., Mazón, J. L. V., Díez, I. D. L. T., & Ashraf, I. (2022) · Sensors · doi:10.3390/s22124556 · Cited by 89 · see citing works
“Although deepfake technology can be utilized for constructive purposes such as filming and virtual reality applications, it has the potential to be used for destructive purposes [6–9].”
BackgroundGodulla, A., Hoffmann, C. P., & Seibert, D. (2021) · Studies in Communication and Media · doi:10.5771/2192-4007-2021-1-72 · Cited by 75 · see citing works
“In addition, through the use of deepfakes, one person can steal another’s identity for financial benefit or to sabotage their reputation (Citron & Chesney, 2018).”
BackgroundInfluentialSeeking Justice and Redress for Victim-Survivors of Image-Based Sexual Abuse
Rackley, E., McGlynn, C., Johnson, K., Henry, N., Gavey, N., Flynn, A., & Powell, A. (2021) · Feminist Legal Studies · doi:10.1007/s10691-021-09460-8 · Cited by 70 · see citing works
Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
Lanzino, R., Fontana, F., Diko, A., Marini, M. R., & Cinque, L. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00381 · Cited by 77 · see citing works
“From fabricated revenge pornog-raphy [5] to doctored political speeches [24], these synthetic creations have the power to deceive, defame, and destabi-lize.”
BackgroundShaaban, O. A., Yıldırım, R., & Alguttar, A. A. (2023) · IEEE Access · doi:10.1109/access.2023.3333866 · Cited by 80 · see citing works
“The utilization of artificially generated or modified audio information poses a significant threat to society, as it has the potential to generate issues of trust when individuals are incapable of distinguishing between authentic and counterfeit material [49].”
BackgroundDeep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future TrendsPreprint
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00378 · Cited by 87 · see citing works
“Other recent works [375], [376] also explored audio synthesis using GANs; however, such work is at the initial stage and will likely open new prospects of future research as it transpired with the use of GANs in the domain of vision (e. g., with DeepFakes [377]).”
BackgroundNon-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries
Umbach, R., Henry, N., Beard, G. F., & Berryessa, C. M. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642382 · Cited by 73 · see citing works
“The societal implications of deepfakes technology are far-reaching and, while many have been recognized or anticipated, experts generally agree current legislative remedies for misuse are unlikely to be effective [15, 20, 34, 52, 73].”
BackgroundDeepfakes for Medical Video De-Identification
Zhu, B., Fang, H., Sui, Y., & Li, L. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375849 · Cited by 69 · see citing works
“Public concerns are growing as deepfake technology may create inappropriate contents[10] and infringe privacy rights[4].”
BackgroundHuman Perception of Audio Deepfakes
Müller, N. M., Pizzi, K., & Williams, J. (2022) · Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia · doi:10.1145/3552466.3556531 · Cited by 68 · see citing works
A literature review and perspectives in deepfakes: generation, detection, and applications
Dagar, D., & Vishwakarma, D. K. (2022) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-022-00241-w · Cited by 73 · see citing works
Data Augmentation-based Novel Deep Learning Method for Deepfaked Images Detection
Iqbal, F., Abbasi, A., Javed, A. R., Almadhor, A. S., Jalil, Z., Anwar, S., & Rida, I. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3592615 · Cited by 70 · see citing works
“Digital image, video, audio recognition, and tampering tools are quickly evolving [1, 2].”
Park, J., Oh, C., & Kim, H. Y. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102705 · Cited by 71 · see citing works
Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2037 · Cited by 71 · see citing works
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Li, C., Huang, Z., Paudel, D. P., Wang, Y., Shahbazi, M., Hong, X., & Gool, L. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00139 · Cited by 69 · see citing works
“As a result, there is a growing threat of “weaponizating” deepfakes for malicious purposes, which is potentially detrimental to privacy, society security and democracy [12].”
BackgroundBeyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry
Tortora, L. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1346059 · Cited by 72 · see citing works
“This phenomenon will create an environment characterised by an overarching sense of distrust, in which parties can weaponise scepticism and doubts to advance their own agendas, a concept also known as the “ liar ’ s dividend ” (124).”
BackgroundDeepFake on Face and Expression Swap: A Review
Waseem, S., Abu Bakar, S. A. R. S., Ahmed, B. A., Omar, Z., Eisa, T. A. E., & Dalam, M. E. E. (2023) · IEEE Access · doi:10.1109/access.2023.3324403 · Cited by 65 · see citing works
“Moreover, watermarking technologies can be integrated into content creation devices, providing a foolproof method of authenticating multimedia files and verifying when and where they were created [12].”
BackgroundMethodInfluentialEnhanced deepfake detection with densenet and cross-vit
Siddiqui, F., Yang, J., Xiao, S., & Fahad, M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126150 · Cited by 62 · see citing works
You never fake alone. Creative AI in action
de Vries, K. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1754877 · Cited by 65 · see citing works
“In this paper I will focus on the civic concern (Chesney & Citron, 2018) that creative AI might undermine the distinction between fact and fi ction.”
BackgroundAI and sustainable tourism: an assessment of risks and opportunities for the SDGs
Gößling, S., & Mei, X. Y. (2025) · Current Issues in Tourism · doi:10.1080/13683500.2025.2477142 · Cited by 74 · see citing works
“The proliferation of AI-generated content raises ethical dilemmas related to mis-information, deepfakes, surveillance, and data privacy, making regulatory oversight increasingly complex (Chesney & Citron, 2019).”
Nygren, T., Guath, M., Axelsson, A., & Frau‐Meigs, D. (2021) · Information · doi:10.3390/info12050201 · Cited by 58 · see citing works
“Pupils rated fake videos and manipulated images as less credible after the intervention, and the intervention did not lead them to rate credible news as less credible, which has previously been noted as a risk that is linked to exposure to fake news [69].”
BackgroundThe promise and peril of deep learning in microscopy
Hoffman, D. P., Slavitt, I., & Fitzpatrick, C. A. (2021) · Nature Methods · doi:10.1038/s41592-020-01035-w · Cited by 58 · see citing works
“Occasionally, this is the goal; consider that the most famous generative network, the so-called Deepfakes model, was originally designed to switch faces in video footage 6 .”
BackgroundImage-Based Sexual Abuse: An Australian Reflection
Flynn, A., & Henry, N. (2019) · Women & Criminal Justice · doi:10.1080/08974454.2019.1646190 · Cited by 56 · see citing works
“It also involves the creation of digitally altered images that depict the victim in a sexual way (Chesney & Citron, 2018).”
BackgroundRini, R., & Cohen, L. (2022) · Journal of Ethics and Social Philosophy · doi:10.26556/jesp.v22i2.1628 · Cited by 55 · see citing works
Surjatmodjo, D., Unde, A. A., Cangara, H., & Sonni, A. F. (2024) · Social Sciences · doi:10.3390/socsci13080418 · Cited by 57 · see citing works
“These technologies enable the creation of increasingly sophisticated fake content that is difficult to distinguish from the real thing, such as manipulative, deep, phony video and audio (Chesney and Citron 2019).”
BackgroundDeepfake attribution: On the source identification of artificially generated images
Khoo, B., Phan, R. C. ‐., & Lim, C. H. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1438 · Cited by 56 · see citing works
“However, their potential to be used in committing cybercrime, inciting social unrest, and subverting political systems is alarming researchers and lawmakers alike (Chesney & Citron, 2018; Westerlund, 2019).”
BackgroundForensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection
Cui, X., Li, Y., Luo, A., Zhou, J., & Dong, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01789 · Cited by 58 · see citing works
“These techniques enable the manipulation of faces with high realism, causing serious concerns to social security, such as privacy violations [7, 34], economic fraud [5, 31], etc.”
ADD: Attention-Based DeepFake Detection Approach
Khormali, A., & Yuan, J.-S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5040049 · Cited by 55 · see citing works
Hybrid Deep Learning Model Based on GAN and RESNET for Detecting Fake Faces
Safwat, S., Mahmoud, A., Fattoh, I. E., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3416910 · Cited by 53 · see citing works
“The deployment of realistic Deepfake images could be dangerous for people’s privacy, democratic processes, and the nation’s security [14].”
BackgroundA systematic literature review on deepfake detection techniques
Sharma, V. K., Garg, R. K., & Caudron, Q. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19906-1 · Cited by 61 · see citing works
“For example, students could watch realistic reenactments of historical events or lectures from virtual representations of famous scientists [16].”
Don't trust your eyes: image manipulation in the age of DeepFakes
Langguth, J., Pogorelov, K., Brenner, S., Filkuková, P., & Schroeder, D. T. (2021) · Frontiers in Communication · doi:10.3389/fcomm.2021.632317 · Cited by 51 · see citing works
“…cyberstalking, online harassment and hate speech, etc., has shown, introducing new laws or changing existing laws to deal with deepfakes might lead to a whole new set of problems, such as hastily executed self-censorship by social media companies to avoid fi nes (Chesney and Citron, 2019b).”
BackgroundInfluentialFace/Off: Changing the face of movies with deepfakes
Murphy, G., Ching, D., Twomey, J., & Linehan, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287503 · Cited by 50 · see citing works
“sensual deepfake pornography remains a pressing issue [6] As well as this, there are concerns that widespread use of deepfake technology could give rise to a society where individuals lose faith in the veracity of media [7] and where video evidence cannot be used in court cases [8,9].”
BackgroundDefending against GAN-based DeepFake Attacks via Transformation-aware Adversarial Faces
Yang, C., Ding, L., Chen, Y., & Li, H. H. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533868 · Cited by 49 · see citing works
“Deepfake can have potentially drastic security consequences if applied inappropriately [5].”
BackgroundKokas, A. (2022) · doi:10.1093/oso/9780197620502.001.0001 · Cited by 51 · see citing works
Weikmann, T., & Lecheler, S. K. (2023) · Digital Journalism · doi:10.1080/21670811.2023.2194665 · Cited by 49 · see citing works
“This, in turn, feeds into the so-called “liar’s dividend” (Ajder and Glick 2021; Chesney & Citron, 2019), meaning that politicians might instrumentalize deepfakes’ existence to claim they did not do or say something.”
Battista, D. (2024) · Society Register · doi:10.14746/sr.2024.8.2.01 · Cited by 50 · see citing works
“This aspect is particularly critical in electoral contexts, where manipulating viral videos containing false statements by public figures can be a powerful tool for disinformation and manipulation of public opinion (Chesney & Citron, 2019).”
BackgroundKhan, S. A., & Dang-Nguyen, D.-T. (2024) · IEEE Access · doi:10.1109/access.2023.3348450 · Cited by 53 · see citing works
“…associate editor coordinating the review of this manuscript and approving it for publication was Wenming Cao . stantial dangers when harnessed for malicious intentions, like crafting convincing fraudulent media to sway public opinion, manipulate electoral outcomes, or incite violence [2], [3], [4].”
BackgroundAxelsson, A., Guath, M., & Nygren, T. (2021) · Future Internet · doi:10.3390/fi13030060 · Cited by 50 · see citing works
“Researchers have noted that there may be a risk that truth decay in news may turn into a trust decay towards credible news [46].”
BackgroundEfficientNets for DeepFake Detection: Comparison of Pretrained Models
Pokroy, A. A., & Egorov, A. D. (2021) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/elconrus51938.2021.9396092 · Cited by 48 · see citing works
“In the age of information technologies, when social networks are actively used as a source of information, media data modified in this way can spread rapidly and have a serious social impact [3, 4].”
BackgroundEffective De-identification Generative Adversarial Network for Face Anonymization
Kuang, Z., Liu, H., Yu, J., Tian, A., Wang, L., Jianping Fan, B., & Babaguchi, N. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475464 · Cited by 47 · see citing works
DeepFake the menace: mitigating the negative impacts of AI-generated content
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One-Shot Domain Adaptation for Face Generation
Yang, C., & Lim, S.-N. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00596 · Cited by 47 · see citing works
“In this paper, we are interested in detecting Deepfakes, which refers to manipulations to replace the human face of authentic media with the face of a different person [40, 10, 13].”
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers
Thing, V. L. L. (2023) · 2023 IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10225004 · Cited by 48 · see citing works
“As these deepfakes targeted at individuals and institutions are made widely and readily available on social media platforms, they can lead to serious political, social, financial and legal consequences [6].”
BackgroundDeepfake Detection Using Spatiotemporal Transformer
Kaddar, B., Fezza, S. A., Akhtar, Z., Hamidouche, W., Hadid, A., & Serra-Sagrista, J. S. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3643030 · Cited by 51 · see citing works
“Despite their many interesting and positive applications, such as film-making and virtual reality [45], the growing harms and malicious uses of deepfake technology have various negative impacts on privacy, social stability, and national security [13].”
- Authors
- Chesney, R., & Citron, D. K.
- Affiliations
- The University of Texas at Austin (US)
- University of Virginia (US)
- Corresponding author
- Chesney, Robert
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2018
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W2883289607
- Semantic Scholar
- c20119f9772907a7dc74cf59deae610af885f3ac
- Cited by
- 880
- References
- 25
- Citations by year
- 2026: 159 · 2025: 168 · 2024: 139 · 2023: 140 · 2022: 95 · 2021: 73 · 2020: 62 · 2019: 44
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- eYLS (Yale Law School) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- deepfakes, synthetic media, privacy, democracy, national security, disinformation, truth decay, cognitive biases, civil liability, criminal sanctions, regulation, authentication, economic sanctions, covert operations, law and policy
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