The Future Unmarked: Watermark Removal in AI-Generated Images via Next-Frame Prediction
Huming Qiu, Zhaoxiang Wang, Mi Zhang, Xiaohan Zhang, Xiaoyu You, Min Yang
Image watermarking embeds imperceptible signals into AI-generated images for deepfake detection and provenance verification. Although recent semantic-level watermarking methods demonstrate strong resistance against conventional pixel-level removal attacks, their robustness against more advanced removal strategies remains underexplored, raising concerns about their reliability in practical scenarios. Existing removal attacks primarily operate in the pixel domain without altering image semantics, which limits their effectiveness against semantic-level watermarks.In this paper, we propose Next Frame Prediction Attack (NFPA), the first semantic-level removal attack. Unlike pixel-level attacks, NFPA formulates watermark removal as a video generation task: it treats the watermarked image as the initial frame and aims to subtly manipulate the image semantics to generate the next-frame image, i.e., the unwatermarked image.We conduct a comprehensive evaluation on eight state-of-the-art semantic watermarking schemes, demonstrating that NFPA consistently outperforms nine removal attack baselines in both watermark removal and image quality preservation. Our results reveal the vulnerabilities of current image watermarking methods and highlight the urgent need for more robust watermarks.Code is available at https://anonymous.4open.science/r/NFPA/.