Fast and Effective Overwrite Attack Against DNN-Based Image Watermarking Models

Deep neural network (DNN)-based image watermarking models have been widely recognized as an effective way to manage the huge amount of AI-generated images. However, the vulnerability of such models to different forms of adversarial attacks has been a critical concern. Among the existing forms of attacks in the literature, image-dependent attacks cannot launch real-time attacks on a large number of watermarked images, because they need to train a new noise image to attack each new watermarked image; image-regeneration attacks either require a lot of information about the watermarking system or cause too much damage to the attacked image. To fill the gap in the existing forms of attacks, in this paper, we propose a novel form of attack named fast and effective overwrite attack (FEOA), which achieves an extremely fast attack speed and strong attack effectiveness. In particular, we discovered a single noise image, when directly added to many watermarked images, can overwrite their true watermark messages to different ones in milliseconds. We also develop an adaptive version of FEOA, which trains $k$ different noise images and applies the principle of divide and conquer to significantly improve attack effectiveness. Our work opens the door to quickly launching massive overwrite attacks on a large number of watermarked images, revealing a new robustness issue of DNN-based image watermarking models. Extensive experiments demonstrate the outstanding attack time efficiency and effectiveness of our methods.