Open Access Open Access  Restricted Access Subscription Access

Deepfake Technology: An Overview, Applications, Detection, and Future Challenges

Diljith M S, Emilyn C P, Fathimathul Afitha Abu T, Salkala K S

Abstract


Deepfake technology, powered by artificial intelligence, has revolutionized digital media by enabling the creation of highly realistic synthetic videos, images, and audio. While it offers numerous benefits in fields such as entertainment, education, and accessibility, deepfake technology also raises significant ethical, legal, and security concerns. This report explores the methods used to generate deepfakes, including Generative Adversarial Networks (GANs) and autoencoders, and highlights key deepfake techniques such as face-swapping, lip-syncing, and voice cloning. It further examines both positive applications such as film production and virtual assistants and negative implications, including misinformation, identity theft, and political manipulation. The increasing prevalence of deepfakes has led to the development of various detection techniques, including AI-driven approaches like convolutional neural networks (CNNs), motion artifact detection, and physiological signal analysis, as well as forensic methods such as metadata analysis and audio forensics. Additionally, the report discusses legal and ethical challenges, regulatory efforts, and technological advancements aimed at mitigating deepfake-related risks. As deepfake technology continues to evolve, a multi-faceted approach involving AI detection, legislative measures, and public awareness is crucial to addressing its potential threats while harnessing its benefits for constructive use.


Full Text:

PDF

References


Korshunov, P., & Marcel, S. (2018). Deepfakes: A new threat to face recognition? Assessment and detection.

Albahar, J. A. M. (2019). Deepfakes threats and countermeasures: Systematic review. JTAIT, 97(22), 3242-3250.

Cellan-Jones, R. (2020). Deepfake videos 'double in nine months'. BBC News.

Citron, D. (2020). TED Talk — Danielle Citron.

BBC Bitesize. (2020). Deepfakes: What are they and why would I make one?

Stamm, M., & Liu, K. (2010). Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Transactions on Information Forensics and Security, 5(3), 492-506.

Stehouwer, J., Dang, H., Liu, F., Liu, X., & Jain, A. (2019). On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717.

Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). FaceForensics++: Learning to detect manipulated facial images. In Proceedings of the International Conference on Computer Vision (pp. 1-10).

Fletcher, J. (2018). Deepfakes, artificial intelligence, and some kind of dystopia: The new faces of online post-fact performance. Theatre Journal, 70(4), 455–471.

Guo, Y., Jiao, L., Wang, S., Wang, S., & Liu, F. (2018). Fuzzy sparse autoencoder framework for single image per person face recognition. IEEE Transactions on Cybernetics, 48(8), 2402-2415.


Refbacks

  • There are currently no refbacks.