{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104943"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104943","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adversarial attacks and defenses for generative models","abstract":"Adversarial Machine learning is a field of research lying at the intersection of Machine Learning and Security, which studies vulnerabilities of Machine learning models that make them susceptible to attacks. The attacks are inflicted by carefully designing a perturbed input which appears benign, but fools the models to perform in unexpected ways. To date, most work in adversarial attacks and defenses has been done for classification models. However, generative models are susceptible to attacks as well, and thus warrant attention. We study some attacks for generative models like Autoencoders and Variational Autoencoders. We discuss the relative effectiveness of the attack methods, and explore some simple defense methods against the attacks.","abstract_html":"Adversarial Machine learning is a field of research lying at the intersection of Machine Learning and Security, which studies vulnerabilities of Machine learning models that make them susceptible to attacks. The attacks are inflicted by carefully designing a perturbed input which appears benign, but fools the models to perform in unexpected ways. To date, most work in adversarial attacks and defenses has been done for classification models. However, generative models are susceptible to attacks as well, and thus warrant attention. We study some attacks for generative models like Autoencoders and Variational Autoencoders. We discuss the relative effectiveness of the attack methods, and explore some simple defense methods against the attacks.","abstract_has_math":false,"creators":["Agarwal, Rishika"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Sanmi","Li, Bo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:05:23Z","date_published":"2019-08-23T20:05:23Z","updated_at":"2026-07-22T22:24:42Z","subjects":["Adversarial Machine learning, generative models, attacks, defenses"],"languages":["en"],"rights":["Copyright 2019 Rishika Agarwal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104943","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Sanmi","Li, Bo"]},{"key":"dc:creator","label":"Author","values":["Agarwal, Rishika"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:05:23Z","2019-04-26","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Adversarial Machine learning, generative models, attacks, defenses"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Rishika Agarwal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104943"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Adversarial Machine learning is a field of research lying at the intersection of Machine Learning and Security, which studies vulnerabilities of Machine learning models that make them susceptible to attacks. The attacks are inflicted by carefully designing a perturbed input which appears benign, but fools the models to perform in unexpected ways. To date, most work in adversarial attacks and defenses has been done for classification models. However, generative models are susceptible to attacks as well, and thus warrant attention. We study some attacks for generative models like Autoencoders and Variational Autoencoders. We discuss the relative effectiveness of the attack methods, and explore some simple defense methods against the attacks.","Submission original under an indefinite embargo labeled 'Open Access'. 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To date, most work in adversarial attacks and defenses has been done for classification models. However, generative models are susceptible to attacks as well, and thus warrant attention. We study some attacks for generative models like Autoencoders and Variational Autoencoders. We discuss the relative effectiveness of the attack methods, and explore some simple defense methods against the attacks.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Rishika Agarwal, accepted the attached license on 2019-04-25 at 16:14.","The student, Rishika Agarwal, submitted this Thesis for approval on 2019-04-25 at 16:21.","This Thesis was approved for publication on 2019-04-26 at 09:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13928 on 2019-08-22 at 14:46:53","Made available in DSpace on 2019-08-23T20:05:23Z (GMT). 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