Abstract: Current facial manipulation technologies have advanced to the point where they can easily modify facial attributes, making it difficult for the human eye to distinguish between real and fake images. Facial image data is readily accessible and can be exploited to forge human faces, posing a constant threat to users’ personal privacy and information security. Consequently, leveraging adversarial defense methods to prevent facial images from being manipulated has become an active area of current research. However, most existing methods primarily focus on the defensive effectiveness against adversarial perturbations added to images, lacking in-depth analysis of scenarios where these adversarial perturbations are subsequently disrupted. To address this gap, this paper proposes an adversarial defense method for facial manipulation targeting image post-processing. By conducting a comprehensive and in-depth analysis of original images, images with adversarial perturbations, and images with disrupted adversarial perturbations, an image adversarial defense model based on contrastive learning is constructed. A thorough comparison and evaluation of the proposed adversarial defense method were conducted, and the experimental results demonstrate that the proposed method exhibits effective defense capabilities against facial manipulation.

Key words: facial manipulation defense, image post-processing, adversarial defense, contrastive learning