1. Veritas
  2. All Volumes and Issues
  3. Volume 1, Number 2, 2025
  4. Deepfakes: Definition of the Concept and Criteria for Distinguishing Between Harmful and Harmless Deepfakes

Deepfakes: Definition of the Concept and Criteria for Distinguishing Between Harmful and Harmless Deepfakes

Veritas.
2025;
Volume 1, Number 2
: 12-20

Hachkevych A. (2025). Deepfakes: Definition of the Concept and Criteria for Distinguishing Between Harmful and Harmless Deepfakes. Veritas: Legal and Psychological-Pedagogical Research. 1(2), 12–20. DOI:

Authors:
  1. Andrii Hachkevych
1
Lviv Polytechnic National University, Ukraine

Abstract. This article addresses the issue of combating deepfakes, which has recently gained significant
relevance. With the emergence of publicly available artificial intelligence tools capable of generating highly
convincing images, video clips and other types of content, as well as a favorable digital landscape for their
dissemination, deepfake technology has become increasingly prevalent. Given the risks of deepfakes,
reasonable expectations are placed on the law designed to protect our fundamental values, which are often
jeopardized by deepfakes. The spread of harmful deepfakes poses risks to the individuals depicted, causes
damage and destroys the reputation of affected organizations, and can be dangerous for society by serving as an
effective means of disinformation and manipulation of public opinion. Therefore, the author examines
deepfakes as a challenge, exploring the concept in depth and highlighting some of the contentious issues. This
article outlines four criteria to differentiate harmful deepfakes from harmless ones: a) consent of the
individual–whether a person featured in a deepfake has agreed to its creation and dissemination, b) absence of
criminal acts–whether a deepfake involves any illegal activities, c) indication of artificial intelligence usage–
whether a deepfake clearly demonstrates characteristics of being created by artificial intelligence,
d) understanding of their intents considering serving a social good. The author also attempts to provide his own
definition of the concept of a deepfake, and to outline its components: technological and intellectual. Alongside
traditional images and videos, he considers that audio recordings should be regarded as a type of deepfakes.
Furthermore, this article discusses the relationship between the dissemination of harmful deepfakes and
relevant legal categories such as revenge porn, defamation, right to privacy, unfair competition, and
disinformation known in contemporary legal systems.

deepfake
definition of deepfake
harmful deepfakes
artificial intelligence
deep machine learning
revenge porn
defamation
right to privacy
disinformation
deepfake law
  1. Alanazi, S., Asif, S. (2024). Exploring deepfake technology: creation, consequences and countermeasures. Human-Intelligent Systems Integration, 6, 49-60. https://doi.org/10.1007/s42454-024-00054-8 
  2. Alanazi, S., Asif, S., Moulitsas, I. (2024) Examining the societal impact and legislative requirements of deepfake technology: a comprehensive study. International Journal of Social Science and Humanity, 14(2), 58-64.
  3. Almars, A. (2021). Deepfakes Detection Techniques Using Deep Learning: A Survey. Journal of Computer and Communications, 9, 20-35. https://doi.org/10.4236/jcc.2021.95003
  4. Anstey, B. J. (2017). Criminal defamation and reputation as 'honour': a cross-jurisdictional perspective. Journal of Media Law, 9(1), 132–153. https://doi.org/10.1080/17577632.2017.1311467
  5. Arslan, F. (2023). Deepfake Technology: A Criminological Literature Review. Sakarya Üniversitesi Hukuk Fakültesi Dergisi, 11(1), 701-720. https://doi.org/10.56701/shd.1293642.
  6. Blancaflor, E., Garcia, J.I., Magno, F.D., & Vilar, M.J. (2024). Deepfake Blackmailing on the Rise: The Burgeoning Posterity of Revenge Pornography in the Philippines. In Proceedings of the 2024 9th International Conference on Intelligent Information Technology (ICIIT '24). Association for Computing Machinery, New York, NY, USA, 295–301. https://doi.org/10.1145/3654522.3654548
  7. Botha, J.G. & Pieterse, H. (2020). Fake news and deepfakes: A dangerous threat for 21st century information security. In Payne, B. and Wu, H. (Eds.), Proceedings of the 15th International Conference on Cyber Warfare and Security (pp. 57-66). 
  8. Campbell, C., Plangger, K., Sands, S., Kietzmann, J., & Bates, K. (2022). How Deepfakes and Artificial Intelligence Could Reshape the Advertising Industry: The Coming Reality of AI Fakes and Their Potential Impact on Consumer Behavior. Journal of Advertising Research, 62(3), 241–251. https://doi.org/10.2501/JAR-2022-017
  9. Chadha, A., Kumar, V., Kashyap, S., Gupta, M. (2021). Deepfake: An Overview. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (Eds.), Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Springer. https://doi.org/10.1007/978-981-16-0733-2_39
  10. Chesney, R. & Citron, D. (2019). Deepfakes and the New Disinformation War. Foreign Affairs, 98(1), 147-155. 
  11. Dogan, S., Lemley, M. (2006). What the Right of Publicity Can Learn from Trademark Law. Stanford Law Review, 58, 1161-1220.
  12. European Commission. (2023, October 30). Hiroshima Process International Guiding Principles for Advanced AI system. Retrieved from https://digital-strategy.ec.europa.eu/en/library/hiroshima-process-inter...
  13. Franks, M.A. (2017). 'Revenge Porn' Reform: A View from the Front Lines. Florida Law Review, 69, 1251-1287.
  14. Gamage, D., Sewwandi, D., Zhang, M., & Bandara, A. (2025). Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media. Retrived from https://arxiv.org/abs/2503.05711
  15. Hancock, J., & Bailenson, N. (2020). The Social Impact of Deepfakes. Cyberpsychology, Behavior, and Social Networking, 24(3), 149-152. https://doi.org/10.1089/cyber.2021.29208.jth
  16. Kaur, A., Hoshyar, A.N., Wang, X., & Xia, F. (2024). Beyond Deception: Exploiting Deepfake Technology for Ethical Innovation in Healthcare. In Proceedings of the 1st International Workshop on Multimedia Computing for Health and Medicine (pp. 70-78). Association for Computing Machinery. https://doi.org/10.1145/3688868.3689196
  17. Kidd, J. & Rees, A. (2022). Chapter 10: A Museum of Deepfakes? Potentials and Pitfalls for Deep Learning Technologies. In T. Stylianou-Lambert, A. Bounia & A. Heraclidou (Ed.), Emerging Technologies and Museums: Mediating Difficult Heritage (pp. 218-232). Berghahn Books. https://doi.org/10.1515/9781800733756-012
  18. Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135-146. https://doi.org/10.1016/j.bushor.2019.11.006
  19. Kikerpill, K., Siibak, A. & Valli, S. (2021). Dealing with Deepfakes: Reddit, Online Content Moderation, and Situational Crime Prevention. In J.B. Wiest (Ed.), Theorizing Criminality and Policing in the Digital Media Age (Studies in Media and Communications, Vol. 20) (pp. 25-45). Emerald Publishing Limited. https://doi.org/10.1108/S2050-206020210000020008
  20. Kirchengast, T. (2020). Deepfakes and image manipulation: criminalisation and control. Information & Communications Technology Law, 29(3), 308–323. https://doi.org/10.1080/13600834.2020.1794615
  21. MacKenzie, W. I., Jr, Weber, R., Barr, H. M., Lanius, C., & Tenhundfeld, N. L. (2025). Deepfake Label Recall: Combating Disinformation with Labels is Especially Effective for Those Who Dislike the Speaker. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2025.2466068
  22. Maddocks, S. (2020). 'A Deepfake Porn Plot Intended to Silence Me': exploring continuities between pornographic and ‘political’ deep fakes. Porn Studies, 7(4), 415–423. https://doi.org/10.1080/23268743.2020.1757499
  23. Masood, M., Nawaz, M., Malik, K.M., Javed, A., Irtaza, A., & Malik, H. (2023). Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Applied Intelligence, 53, 3974–4026. https://doi.org/10.1007/s10489-022-03766-z
  24. McCarthy, T. (1995). The Human Persona as Commercial Property: The Right of Publicity. Columbia University School of Law and the Arts, 19(3-4), 129-148.
  25. McKenna, M. (2005). The Right of Publicity and Autonomous Self-Definition. University of Pittsburgh Law Review, 67, 225-294.
  26. Meikle, G. (2022). Deepfakes. John Wiley & Sons.
  27. Meskys, E., Liaudanskas, A., Kalpokiene, J., & Jurcys, P. (2019). Regulating deep fakes: Legal and ethical considerations. Journal of Intellectual Property Law & Practice, 15(1), 24-31.  https://doi.org/10.1093/jiplp/jpz167 
  28. Miller, D., Somoray, K., & Stevens, H. (2025). A Shallow History of Deepfakes (January 22, 2025). Available at SSRN: https://papers.ssrn.com/sol3/Delivery.cfm/5130379.pdf?abstractid=5130379...
  29. Merriam-Webster. (n.d.) Revenge Porn. In Merriam-Webster.com dictionary. Retrieved March 10, 2025, from https://www.merriam-webster.com/dictionary/revenge%20porn
  30. Mirsky, Y. & Lee, W. (2021). The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys, 54(1), 7. https://doi.org/10.1145/3425780
  31. Moskalenko, K. (2015). The right of publicity in the USA, the EU, and Ukraine. International Comparative Jurisprudence, 1(2), 113-120. https://doi.org/10.1016/j.icj.2015.12.001
  32. Mudra, I. (2016). The Concept of “Fake” and its Views in the Media. TV and Radio Journalism, 15, 184-188.
  33. Mullen, M. (2022). A New Reality: Deepfake Technology and the World Around Us. Mitchell Hamline Law Review, 48(1), 210-234.
  34. Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023). Deepfakes: Deceptions, mitigations, and opportunities. Journal of Business Research, 154, Article 113368. https://doi.org/10.1016/j.jbusres.2022.113368
  35. Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q., & Nguyen, C. M. (2022). Deep learning for deepfakes creation and detection: A survey. Computer Vision and Image Understanding, 223, 103525. https://doi.org/10.1016/j.cviu.2022.103525
  36. Pandey, C.K., Mishra, V.K., & Tiwari, N.K. Deepfakes: When to Use It. In 2021 10th International Conference on System Modeling & Advancement in Research Trends (pp. 80-84). IEEE. https://doi.org/10.1109/SMART52563.2021.9676297
  37. Sandoval, M.P., de Almeida Vau, M., Solaas, J., Rodrigues, L. (2024). Threat of deepfakes to the criminal justice system: a systematic review. Crime Science, 13, 41. https://doi.org/10.1186/s40163-024-00239-1
  38. Sharma, M., Kaur, M. (2022). A review of deepfake technology: an emerging AI threat. In Soft Computing for Security Applications: Proceedings of ICSCS 2021 (pp. 605-619). Springer. https://doi.org/10.1007/978-981-16-5301-8_44
  39. Tariq, S., Abuadbba, A., & Moore, K. (2023). Deepfake in the Metaverse: Security Implications for Virtual Gaming, Meetings, and Offices. In Proceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes (pp. 16-19). Association for Computing Machinery. https://doi.org/10.1145/3595353.3595880
  40. Vaccari, C., & Chadwick, A. (2020). Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News. Social Media + Society, 6(1), 1-13. https://doi.org/10.1177/2056305120903408
  41. Vasist, P., & Krishnan, S. (2022). Deepfakes: An Integrative Review of the Literature and an Agenda for Future Research. Communications of the Association for Information Systems, 51, 556-562. https://doi.org/10.17705/1CAIS.05126
  42. Verdoliva, L. (2020). Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910-932. https://doi.org/10.1109/JSTSP.2020.3002101
  43. Wang, S.-Y., Wang, O., Zhang, R., Owens, A., & Efros, A.A. (2020). CNN-Generated Images Are Surprisingly Easy to Spot… for Now. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8692-8701). Seattle. https://doi/10.1109/CVPR42600.2020.00872
  44. Werner, J. (2024, October 24). Singapore's parliament passes bill to combat manipulated online election content. Retrieved from https://babl.ai/singapores-parliament-passes-bill-to-combat-manipulated-...
  45. Wittenberg, C., Epstein, Z., Berinsky, A. J., & Rand, D. G. (2024). Labeling AI-Generated Content: Promises, Perils, and Future Directions. An MIT Exploration of Generative AI. https://doi.org/10.21428/e4baedd9.0319e3a6
  46. Westerlund M. (2019). The Emergence of Deepfake Technology: A Review. Technology Innovation Management Review, 9(11), 40-53. 
  47. Whittaker, L., Letheren, K., & Mulcahy, R. (2021). The Rise of Deepfakes: A Conceptual Framework and Research Agenda for Marketing. Australasian Marketing Journal, 29(3), 204-214. https://doi.org/10.1177/1839334921999479
  48. Zhu, B., Fang, H., Sui, Y., & Li, L. (2020). Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 414-420). Association for Computing Machinery. https://doi.org/10.1145/3375627.3375849