{"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/feature-squeezing-mitigates-and-detects","title":"Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples","arxiv_id":"1705.10686","date":"2017-05-30","proceeding":null,"authors":["Weilin Xu","David Evans","Yanjun Qi"],"abstract":"Feature squeezing is a recently-introduced framework for mitigating and\ndetecting adversarial examples. In previous work, we showed that it is\neffective against several earlier methods for generating adversarial examples.\nIn this short note, we report on recent results showing that simple feature\nsqueezing techniques also make deep learning models significantly more robust\nagainst the Carlini/Wagner attacks, which are the best known adversarial\nmethods discovered to date.","url_abs":"http://arxiv.org/abs/1705.10686v1","url_pdf":"http://arxiv.org/pdf/1705.10686v1.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":"feature-squeezing-mitigates-and-detects","repo_url":"https://github.com/mzweilin/EvadeML-Zoo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}