Universal and Quality-Preserving Watermark Removal Based on Unpaired Learning

Invisible image watermarking plays a critical role in safeguarding AI-generated images, yet current removal methods face practical limitations in real-world settings. They compromise image quality, are tailored to specific watermarking schemes, or depend on original-watermarked image pairs. These limitations hinder reliable evaluations of watermark robustness. In this work, we propose a universal and quality-preserving watermark removal method based on unpaired learning. Specifically, we implement a three-stage training framework in which we first pre-train the remover to denoise corrupted images. Then the discriminator is trained to distinguish watermarked images from original ones. Finally, the remover and the discriminator are jointly trained in an adversarial manner to further strengthen the watermark elimination capability of the remover while enhancing image quality. Evaluated across various watermarking schemes, our method achieves watermark extraction error rates close to random guessing while maintaining high visual quality. This work reveals that most existing watermarking methods lack sufficient robustness.