Zhimao Lai1,2, Saad Arif3, Cong Feng4, Guangjun Liao5, Chuntao Wang6,*
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 73-102, 2025, DOI:10.32604/cmc.2024.059370
- 03 January 2025
Abstract With the rapid advancement of visual generative models such as Generative Adversarial Networks (GANs) and stable Diffusion, the creation of highly realistic Deepfake through automated forgery has significantly progressed. This paper examines the advancements in Deepfake detection and defense technologies, emphasizing the shift from passive detection methods to proactive digital watermarking techniques. Passive detection methods, which involve extracting features from images or videos to identify forgeries, encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics. In contrast, proactive digital watermarking techniques embed specific markers into images or videos, facilitating More >
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