WAVES: Benchmarking the Robustness of Image Watermarks
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914828767985664 |
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| author | An, Bang Ding, Mucong Rabbani, Tahseen Agrawal, Aakriti Xu, Yuancheng Deng, Chenghao Zhu, Sicheng Mohamed, Abdirisak Wen, Yuxin Goldstein, Tom Huang, Furong |
| author_facet | An, Bang Ding, Mucong Rabbani, Tahseen Agrawal, Aakriti Xu, Yuancheng Deng, Chenghao Zhu, Sicheng Mohamed, Abdirisak Wen, Yuxin Goldstein, Tom Huang, Furong |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WAVES: Benchmarking the Robustness of Image Watermarks An, Bang Ding, Mucong Rabbani, Tahseen Agrawal, Aakriti Xu, Yuancheng Deng, Chenghao Zhu, Sicheng Mohamed, Abdirisak Wen, Yuxin Goldstein, Tom Huang, Furong Computer Vision and Pattern Recognition Cryptography and Security Machine Learning 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/ |
| title | WAVES: Benchmarking the Robustness of Image Watermarks |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2401.08573 |