First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge
This work addresses the robustness of watermarking for copyright protection, highlighting vulnerabilities that could impact media authentication and security, though it is incremental as it builds on existing adversarial attack methods.
The paper tackled the problem of removing invisible watermarks from digital images, presenting the winning solution to the NeurIPS 2024 challenge, which achieved near-perfect removal rates of 95.7% with minimal impact on image quality.
Content watermarking is an important tool for the authentication and copyright protection of digital media. However, it is unclear whether existing watermarks are robust against adversarial attacks. We present the winning solution to the NeurIPS 2024 Erasing the Invisible challenge, which stress-tests watermark robustness under varying degrees of adversary knowledge. The challenge consisted of two tracks: a black-box and beige-box track, depending on whether the adversary knows which watermarking method was used by the provider. For the beige-box track, we leverage an adaptive VAE-based evasion attack, with a test-time optimization and color-contrast restoration in CIELAB space to preserve the image's quality. For the black-box track, we first cluster images based on their artifacts in the spatial or frequency-domain. Then, we apply image-to-image diffusion models with controlled noise injection and semantic priors from ChatGPT-generated captions to each cluster with optimized parameter settings. Empirical evaluations demonstrate that our method successfully achieves near-perfect watermark removal (95.7%) with negligible impact on the residual image's quality. We hope that our attacks inspire the development of more robust image watermarking methods.