TY  - EJOU
AU  - Lai, Zhimao 
AU  - Arif, Saad 
AU  - Feng, Cong 
AU  - Liao, Guangjun 
AU  - Wang, Chuntao 

TI  - Enhancing Deepfake Detection: Proactive Forensics Techniques Using Digital Watermarking
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 1
SN  - 1546-2226

AB  - 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 real-time detection and traceability, thereby providing a preemptive defense against Deepfake content. We offer a comprehensive analysis of digital watermarking-based forensic techniques, discussing their advantages over passive methods and highlighting four key benefits: real-time detection, embedded defense, resistance to tampering, and provision of legal evidence. Additionally, the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions, including cross-domain watermarking and adaptive watermarking strategies. By systematically classifying and comparing existing techniques, this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.
KW  - Deepfake; proactive forensics; digital watermarking; traceability; detection techniques

DO  - 10.32604/cmc.2024.059370