{"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/lavan-localized-and-visible-adversarial-noise","title":"LaVAN: Localized and Visible Adversarial Noise","arxiv_id":"1801.02608","date":"2018-01-08","proceeding":"ICML 2018 7","authors":["Danny Karmon","Daniel Zoran","Yoav Goldberg"],"abstract":"Most works on adversarial examples for deep-learning based image classifiers\nuse noise that, while small, covers the entire image. We explore the case where\nthe noise is allowed to be visible but confined to a small, localized patch of\nthe image, without covering any of the main object(s) in the image. We show\nthat it is possible to generate localized adversarial noises that cover only 2%\nof the pixels in the image, none of them over the main object, and that are\ntransferable across images and locations, and successfully fool a\nstate-of-the-art Inception v3 model with very high success rates.","url_abs":"http://arxiv.org/abs/1801.02608v2","url_pdf":"http://arxiv.org/pdf/1801.02608v2.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":"lavan-localized-and-visible-adversarial-noise","repo_url":"https://github.com/lith0613/LaVAN_python-tf-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.02608","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}