Electronics, Vol. 13, Pages 4955: A Novel Face Swapping Detection Scheme Using the Pseudo Zernike Transform Based Robust Watermarking

Electronics, Vol. 13, Pages 4955: A Novel Face Swapping Detection Scheme Using the Pseudo Zernike Transform Based Robust Watermarking

Electronics doi: 10.3390/electronics13244955

Authors: Zhimao Lai Zhuangxi Yao Guanyu Lai Chuntao Wang Renhai Feng

The rapid advancement of Artificial Intelligence Generated Content (AIGC) has significantly accelerated the evolution of Deepfake technology, thereby introducing escalating social risks due to its potential misuse. In response to these adverse effects, researchers have developed defensive measures, including passive detection and proactive forensics. Although passive detection has achieved some success in identifying Deepfakes, it encounters challenges such as poor generalization and decreased accuracy, particularly when confronted with anti-forensic techniques and adversarial noise. As a result, proactive forensics, which offers a more resilient defense mechanism, has garnered considerable scholarly interest. However, existing proactive forensic methodologies often fall short in terms of visual quality, detection accuracy, and robustness. To address these deficiencies, we propose a novel proactive forensic approach that utilizes pseudo-Zernike moment robust watermarking. This method is specifically designed to enhance the detection and analysis of face swapping by transforming facial data into a binary bit stream and embedding this information within the non-facial regions of video frames. Our approach facilitates the detection of Deepfakes while preserving the visual integrity of the video content. Comprehensive experimental evaluations have demonstrated the robustness of this method against standard signal processing operations and its superior performance in detecting Deepfake manipulations.