{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/natural-color-fool-towards-boosting-black-box","title":"Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks","arxiv_id":"2210.02041","date":"2022-10-05","proceeding":null,"authors":["Shengming Yuan","Qilong Zhang","Lianli Gao","Yaya Cheng","Jingkuan Song"],"abstract":"Unrestricted color attacks, which manipulate semantically meaningful color of an image, have shown their stealthiness and success in fooling both human eyes and deep neural networks. However, current works usually sacrifice the flexibility of the uncontrolled setting to ensure the naturalness of adversarial examples. As a result, the black-box attack performance of these methods is limited. To boost transferability of adversarial examples without damaging image quality, we propose a novel Natural Color Fool (NCF) which is guided by realistic color distributions sampled from a publicly available dataset and optimized by our neighborhood search and initialization reset. By conducting extensive experiments and visualizations, we convincingly demonstrate the effectiveness of our proposed method. Notably, on average, results show that our NCF can outperform state-of-the-art approaches by 15.0%$\\sim$32.9% for fooling normally trained models and 10.0%$\\sim$25.3% for evading defense methods. Our code is available at https://github.com/ylhz/Natural-Color-Fool.","url_abs":"https://arxiv.org/abs/2210.02041v1","url_pdf":"https://arxiv.org/pdf/2210.02041v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"natural-color-fool-towards-boosting-black-box","repo_url":"https://github.com/ylhz/natural-color-fool","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"}],"methods":[{"method_slug":"adversarial-color-enhancement","method_name":"Adversarial Color Enhancement"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.02041","atlas_url":"https://app.syntology.ai/?focus=2210.02041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02041"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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