Robustness of Face Recognition Adversarial Based on Image Denoising

Adversarial attacks have become an essential direction for security research in this area, as they generate adversarial samples to deceive face recognition systems. To defend against adversarial attacks, people need various methods to cope with the attacks, and the methods based on image denoising have widely attracted the attention of scholars for their High applicability and robustness. This paper aims to explore the application of three types of anti-attack defence in the field of face recognition by traditional filtering, JPEG compression denoising and deep learning denoising. The paper concluded the high efficiency of traditional filtering and JPEG compression denoising, with the lack of effect when defending against non-noise type attacking, however, deep learning noise reduction is far superior to traditional filtering or compression noise reduction in effect. On this basis, this paper further explores the insufficient attention to high-frequency noise denoising in the current study, and looks forward to the future research trend of combining image denoising and face recognition security, providing a reference for subsequent research.