Adversarial attack for robust watermark protection against inpainting-based and blind watermark removers

Abstract

The rise of social media platforms, especially those focusing on image sharing, has made visible watermarks increasingly important in protecting image copyrights. However, multiple studies have revealed that watermarks are vulnerable to both inpainting-based removers and blind watermark removers. Though two adversarial attack methods have been proposed to defend against watermark removers, they are tailored to a particular type of removers in a white-box setting, which significantly limits their practicality and applicability. To date, there is no adversarial attack method that can protect watermarks against the two types of watermark removers simultaneously. In this paper, we propose a novel method, named Adversarial Watermark Defender with Attribution-Guided Perturbation (AWD-AGP), that defends against both inpainting-based and blind watermark removers under a black-box setting. AWD-AGP is the first watermark protection method employing adversarial location. The adversarial location is generated by a Watermark Positioning Network, which predicts an optimal location for watermark placement, making watermark removal challenging for inpainting-based removers. Since inpainting-based removers and blind watermark removers exploit information in different regions of an image to perform removal, we propose an attribution-guided scheme, which automatically assigns attack strengths to different pixels against different removers. With this design, the generated perturbation can attack the two types of watermark removers concurrently. Experiments on seven models, including four inpainting-based removers and three blind watermark removers demonstrate the effectiveness of AWD-AGP.