Evolving Deepfake Technologies: Advancements, Detection Techniques, and Societal Impact
DOI:
https://doi.org/10.48165/dbitdjr.2024.1.02.06Keywords:
GAN, CNN, RNNAbstract
The rapid advancement of Deepfake technology, powered by deep learning algorithms and Generative Adversarial Networks (GANs), presents a paradigm shift in digital content creation and manipulation. This technology, capable of generating highly realistic but entirely synthetic audiovisual content, has implications that stretch across various domains, from entertainment to politics, posing both opportunities for innovation and risks for misinformation and privacy violations. This paper provides a comprehensive overview of the evolution of Deepfake technology, highlighting key developments in AI- driven synthetic media creation. It delves into the state-of-the-art detection techniques that leverage both appearance-based and geometric features to combat the proliferation of Deepfakes, emphasizing the importance of precise geometric analysis and temporal modelling in enhancing detection robustness, especially in the face of sophisticated manipulation techniques that evade traditional detection methods. Through an analysis of contemporary datasets and detection frameworks, the paper assesses the effectiveness and limitations of current approaches, underscoring the challenges posed by video compression and digital noise in real-world scenarios. Furthermore, it discusses the profound societal impact of Deepfakes, from the erosion of trust in digital media to legal and ethical dilemmas, and proposes future directions for both technological advancements in Deepfake generation and detection, and policy measures to mitigate their adverse effects. By bridging the gap between technological capabilities and ethical considerations, the research aims to foster a deeper understanding of Deepfakes’ dual potential to both enrich and deceive, calling for a balanced approach to harnessing and regulating this powerful technology.References
[1] S. Das, S. Seferbekov, A. Datta, M. S. Islam, and M. R. Amin, “Towards Solving the DeepFake Problem : AAn Analysis on Improving DeepFake Detection using Dynamic Face Augmentation,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2021-Octob, pp. 3769–3778, 2021, doi: 10.1109/
ICCVW54120.2021.00421.
[2] L. Guarnera, O. Giudice, and S. Battiato, “Mastering Deepfake Detection : A Cuting-Edge Approach to Distinguish GAN and Difusion-Model Images”, doi: 10.1145/3652027.
[3] L. Guarnera, O. Giudice, and S. Battiato, “Fighting deepfake by exposing the convolutional traces on images,” IEEE Access, vol. 8, pp. 165085–165098, 2020, doi: 10.1109/ ACCESS.2020.3023037.
[4] Z. Sun, Y. Han, Z. Hua, N. Ruan, and W. Jia, “Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 3608–3617, 2021, doi: 10.1109/CVPR46437.2021.00361.
[5] S. Ramachandran, A. V. Nadimpalli, and A. Rattani, “An Experimental Evaluation on Deepfake Detection using Deep Face Recognition,” Proc. - Int. Carnahan Conf.
Secur. Technol., vol. 2021-Octob, 2021, doi: 10.1109/ ICCST49569.2021.9717407.
[6] M. S. Rana, M. N. Nobi, B. Murali, and A. H. Sung, “Deepfake Detection: A Systematic Literature Review,” IEEE Access, vol. 10, pp. 25494–25513, 2022, doi: 10.1109/ ACCESS.2022.3154404.
[7] S. Lyu, “Deepfake detection: Current challenges and next steps,” 2020 IEEE Int. Conf. Multimed. Expo Work. ICMEW 2020, 2020, doi: 10.1109/ICMEW46912.2020.9105991.
[8] B. Dolhansky et al., “The DeepFake Detection Challenge (DFDC) Dataset,” 2020, [Online]. Available: http://arxiv.org/ abs/2006.07397
[9] H. F. Shahzad, F. Rustam, E. S. Flores, J. Luís Vidal Mazón, I. de la Torre Diez, and I. Ashraf, “A Review of Image Processing Techniques for Deepfakes,” Sensors, vol. 22, no. 12, pp. 1–28, 2022, doi: 10.3390/s22124556.
[10] A. Rahman, M. Islam, M. J. Moon, T. Tasnim, and N. Siddique, “A Qualitative Survey on Deep Learning Based Deep fake Video Creation and Detection Method,” Aust. J. Eng. Innov. Technol., vol. 4, no. 1, pp. 13–26, 2022, doi: 10.34104/
ajeit.022.013026.
[11] A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Niessner, “FaceForensics++: Learning to detect manipulated facial images,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2019- Octob, pp. 1–11, 2019, doi: 10.1109/ICCV.2019.00009.
[12] M. Taeb and H. Chi, “Comparison of Deepfake Detection Techniques through Deep Learning,” J. Cybersecurity Priv., vol. 2, no. 1, pp. 89–106, 2022, doi: 10.3390/jcp2010007.
[13] J. Hu, X. Liao, J. Liang, W. Zhou, and Z. Qin, “FInfer: Frame Inference-Based Deepfake Detection for High-Visual- Quality Videos,” Proc. 36th AAAI Conf. Artif. Intell. AAAI 2022, vol. 36, pp. 780–789, 2022, doi: 10.1609/aaai.v36i1.19978.
[14] C. Tan, Y. Zhao, S. Wei, G. Gu, P. Liu, and Y. Wei, “Frequency Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning,” pp. 5052–5060, 2024, doi: 10.1609/aaai.v38i5.28310.
[15] S. Agarwal, H. Farid, Y. Gu, M. He, K. Nagano, and H. Li, “Protecting world leaders against deep fakes,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2019-June, pp. 38–45, 2019.
[16] A. Godulla, C. P. Hoffmann, and D. M. A. Seibert,
“Dealing with deepfakes - An interdisciplinary examination
of the state of research and implications for communication
studies,” Stud. Commun. Media, vol. 10, no. 1, pp. 73–96,
2021, doi: 10.5771/2192-4007-2021-1-72.
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