{"id":713749,"date":"2020-12-22T08:57:08","date_gmt":"2020-12-22T16:57:08","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=713749"},"modified":"2021-11-17T17:29:47","modified_gmt":"2021-11-18T01:29:47","slug":"unadversarial-examples-designing-objects-for-robust-vision","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/unadversarial-examples-designing-objects-for-robust-vision\/","title":{"rendered":"Unadversarial Examples: Designing Objects for Robust Vision"},"content":{"rendered":"<p>We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized. We develop a framework that leverages this capability to significantly improve vision models\u2019 performance and robustness. This framework exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design \u201crobust objects,\u201d i.e., objects that are explicitly optimized to be confidently detected or classified. We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks, to (in-simulation) robotics, to real-world experiments. Our code can be found on <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/microsoft\/unadversarial\" target=\"_blank\" rel=\"noopener noreferrer\">GitHub<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized. We develop a framework that leverages this capability to significantly improve vision models\u2019 performance and robustness. This framework exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design \u201crobust objects,\u201d [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-713749","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-12","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/12\/main_unadv.pdf","id":"713824","title":"main_unadv","label_id":"252679","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2012.12235","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":713824,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/12\/main_unadv.pdf"}],"msr-author-ordering":[{"type":"edited_text","value":"Hadi Salman*","user_id":38230,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Hadi Salman*"},{"type":"text","value":"Andrew Ilyas*","user_id":0,"rest_url":false},{"type":"text","value":"Logan Engstrom*","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Sai Vemprala","user_id":39180,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sai Vemprala"},{"type":"text","value":"Aleksander Madry","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Ashish Kapoor","user_id":30903,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ashish 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