The swift development of generative artificial intelligence technology has led to the production of realistic audio, images, and videos generated synthetically, which is known as deepfakes. Even though these technologies have some legitimate uses, their sophistication leads to serious issues regarding the validity of digital evidence presented during judicial proceedings. Deepfakes can manipulate facial expressions, voices, movements, and occurrences with such realism that the verification through visual inspection alone cannot establish its legitimacy. Thus, there arises an important evidentiary challenge where digital evidence that is realistic no longer presents a real person or occurrence. This paper discusses the evidentiary challenges raised by deepfakes to the legitimacy and admissibility of digital evidence within the context of Indian law with special focus on Bharatiya Sakshya Adhiniyam, 2023 and jurisprudence on electronic evidence. The paper also addresses the use of AI-based deepfake detection and digital forensics for the validation of the manipulated digital media. It is argued that, while legal admissibility criteria may suffice to mitigate AI-enabled manipulation to some degree, without additional technical verification, this approach is unlikely to be effective against complex forms of manipulation. Therefore, a techno-legal approach to authentication combining such factors as provenance verification, metadata and integrity checks, forensics, AI-supported deep fake detection, expert evaluation, and chain of custody protection has been suggested.
- Anvar P.V. v. P.K. Basheer and Others, 10 Supreme Court of India 473 (2014).
- Arjun Panditrao Khotkar v. Kailash Kushanrao Gorantyal and Others, 7 Supreme Court of India 1 (2020).
- Chesney, R., & Citron, D. K. (2019). Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. California Law Review, 107(6), 1753–1820.
- Gong, L. Y., & Li, X. J. (2024). A Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges. Electronics, 13(3), 585.
-
- Khan, S. A., & Dang-Nguyen, D.-T. (2023). Deepfake Detection: A Comparative Analysis. https://doi.org/10.48550/arXiv.2308.03471
- Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020). Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3204–3213. https://doi.org/10.1109/CVPR42600.2020.00327
- Lyu, S. (2020). Deepfake Detection: Current Challenges and Next Steps. 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 1–6. https://doi.org/10.1109/ICMEW46912.2020.9105991
- Mirsky, Y., & Lee, W. (2021). The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys, 54(1), 1–41. https://doi.org/10.1145/3425780
- Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024). Deepfake Forensics: A Survey of Digital Forensic Methods for Multimodal Deepfake Identification on Social Media. PeerJ Computer Science, 10, e2037. https://doi.org/10.7717/peerj-cs.2037
- The Bharatiya Sakshya Adhiniyam, 2023 (2023).
- The Information Technology Act, 2000 (2000).
- Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020). DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. Information Fusion, 64, 131–148. https://doi.org/10.1016/j.inffus.2020.06.014
- 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
- Verdoliva, L. and R. C. and T. J. and R. A. and C. D. and N. M. (2019). Faceforensics++: Learning to detect manipulated facial images. Inte. Conf. on Comp. Visi.(ICCV). IEEE/CVF.