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Open Access

REVIEW

Enhancing Deepfake Detection: Proactive Forensics Techniques Using Digital Watermarking

Zhimao Lai1,2, Saad Arif3, Cong Feng4, Guangjun Liao5, Chuntao Wang6,*
1 School of Automation, Guangdong University and Technology, Guangzhou, 510006, China
2 School of Immigration Administration (Guangzhou), China People’s Police University, Guangzhou, 510663, China
3 Department of Mechanical Engineering, College of Engineering, King Faisal University, Al Ahsa, 31982, Saudi Arabia
4 Cyber Police Division, Guangzhou Public Security Bureau, Guangzhou, 510030, China
5 School of Forensic Science and Technology, Guangdong Police College, Guangzhou, 510320, China
6 College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
* Corresponding Author: Chuntao Wang. Email: email
(This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.059370

Received 05 October 2024; Accepted 26 November 2024; Published online 13 December 2024

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 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.

Keywords

Deepfake; proactive forensics; digital watermarking; traceability; detection techniques

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