{"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/a-unified-game-theoretic-interpretation-of","title":"A Unified Game-Theoretic Interpretation of Adversarial Robustness","arxiv_id":"2111.03536","date":"2021-11-05","proceeding":null,"authors":["Jie Ren","Die Zhang","Yisen Wang","Lu Chen","Zhanpeng Zhou","Yiting Chen","Xu Cheng","Xin Wang","Meng Zhou","Jie Shi","Quanshi Zhang"],"abstract":"This paper provides a unified view to explain different adversarial attacks and defense methods, \\emph{i.e.} the view of multi-order interactions between input variables of DNNs. Based on the multi-order interaction, we discover that adversarial attacks mainly affect high-order interactions to fool the DNN. Furthermore, we find that the robustness of adversarially trained DNNs comes from category-specific low-order interactions. Our findings provide a potential method to unify adversarial perturbations and robustness, which can explain the existing defense methods in a principle way. Besides, our findings also make a revision of previous inaccurate understanding of the shape bias of adversarially learned features.","url_abs":"https://arxiv.org/abs/2111.03536v2","url_pdf":"https://arxiv.org/pdf/2111.03536v2.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":"a-unified-game-theoretic-interpretation-of","repo_url":"https://github.com/jie-ren/a-unified-game-theoretic-interpretation-of-adversarial-robustness","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"adversarial-robustness","task_name":"Adversarial Robustness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2111.03536","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}