Reversible adversarial visible image watermarking

Abstract

Visible watermarking serves as a crucial security mechanism for safeguarding the copyright of digital images. Recent advancements, however, have shown that deep neural networks can effectively remove these watermarks without altering the underlying host image, posing a substantial risk to copyright protection. Motivated by the susceptibility of neural networks to adversarial perturbations, various adversarial visible watermarking techniques have been introduced. Nonetheless, these approaches often overlook the need for image reversibility, which is vital for authorized sharing while maintaining privacy. To address this issue, we propose Reversible Adversarial Visible Watermarking (RAVW), which uses Gradient-weighted Class Activation Mapping (Grad-CAM) to pinpoint the important regions in the host image that are optimal for watermark embedding. It then employs an end-to-end generative model to create reversible adversarial visible watermarks within these regions, effectively counteracting watermark removal networks. Additionally, authorized users can eliminate the visible watermark via a dedicated restoration module. Comprehensive experimental evaluations confirm the robustness of our method in preserving visible watermarks and its effectiveness against watermark removal networks.