{"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/robust-network-architecture-search-via","title":"Robust Network Architecture Search via Feature Distortion Restraining","arxiv_id":null,"date":"2022-07-13","proceeding":"ECCV 2022 7","authors":["Yaguan Qian","Shenghui Huang","Bin Wang","Xiang Ling","Xiaohui Guan","Zhaoquan Gu","Shaoning Zeng","WuJie Zhou","Haijiang Wang"],"abstract":"The vulnerability of Deep Neural Networks, i.e., susceptibility to adversarial attacks, severely limits the application of DNNs in security-sensitive domains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust network against adversarial attacks. We observe that an adversarial perturbation distorting the non-robust features in latent feature space can further aggravate misclassification. Based on this observation, we search the robust architecture through restricting feature distortion in the search process. Specifically, we define a network vulnerability metric based on feature distortion as a constraint in the search process. This process is modeled as a multi-objective bilevel optimization problem and a novel algorithm is proposed to solve this optimization. Extensive experiments conducted on CIFAR-10/100 and SVHN show that RNAS achieves the best robustness under various adversarial attacks compared with extensive baselines and SOTA methods.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-031-20065-6_8","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-031-20065-6_8","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":"robust-network-architecture-search-via","repo_url":"https://github.com/hye999/RNAS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bilevel-optimization","task_name":"Bilevel Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}