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WAVES: Benchmarking the Robustness of Image Watermarks

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arxiv 2401.08573 v3 pith:4KYCCSKY submitted 2024-01-16 cs.CV cs.CRcs.LG

Bang An , Mucong Ding , Tahseen Rabbani , Aakriti Agrawal , Yuancheng Xu , Chenghao Deng , Sicheng Zhu , Abdirisak Mohamed
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Yuxin Wen Tom Goldstein Furong Huang
This is my paper
classification cs.CVcs.CRcs.LG
keywords wavesevaluationimageattackswatermarkwatermarksdetectionnovel
verification ladder T0 review T1 audit T2 compute T3 formal
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In the burgeoning age of generative AI, watermarks act as identifiers of provenance and artificial content. We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a benchmark for assessing image watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and identification tasks and establishes a standardized evaluation protocol comprised of a diverse range of stress tests. The attacks in WAVES range from traditional image distortions to advanced, novel variations of diffusive, and adversarial attacks. Our evaluation examines two pivotal dimensions: the degree of image quality degradation and the efficacy of watermark detection after attacks. Our novel, comprehensive evaluation reveals previously undetected vulnerabilities of several modern watermarking algorithms. We envision WAVES as a toolkit for the future development of robust watermarks. The project is available at https://wavesbench.github.io/

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking

    cs.CY 2026-04 conditional novelty 6.0 of 10

    Major watermarking benchmarks omit cross-lingual, cultural, and demographic reporting, creating a pluralistic evaluation gap that current governance mandates ignore.

  2. IConMark: Robust Interpretable Concept-Based Watermark For AI Images

    cs.CV 2025-07 conditional novelty 6.0 of 10

    IConMark adds preselected, human-readable objects to AI images via prompt engineering and detects them with a vision-language model, achieving higher AUROC than noise-based watermarks on tested augmentations.

  3. When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting

    cs.CR 2025-07 reject novelty 4.0 of 10

    Blur-plus-deblur and generator fine-tuning can push watermark bit accuracy toward chance, but only when the attacker can train a surrogate decoder that matches the target's architecture.

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