Imprints: Mitigating Watermark Removal Attacks With Defensive Watermarks

Xiaofu Chen, Jiangyi Deng, Yanjiao Chen, Chaohao Li, Xin Fang, Cong Liu, Wenyuan Xu

Abstract: Watermark is essential for protecting the intellectual property of private images. However, a wide range of watermark removal attacks, especially many AI-powered ones, can automatically predict and remove watermarks, posing serious concerns. In this paper, we present the design of Imprints, a defensive watermarking framework that fortifies watermarks against watermark removal attacks. By formulating an optimization problem that deters watermark removal attacks, we design image-independent/dependent defensive watermark models for effective batch/customized protection. We further enhance the watermark to be transferable to unseen watermark removal attacks and robust to editing distortions. Extensive experiments verify that Imprints outperforms existing baselines in terms of its immunity to 8 state-of-the-art watermark removal attacks and 3 commercial black-box watermark removal software. The source code is available at https://github.com/Imprints-wm/Imprints.