{"created":"2025-01-19T01:29:38.300437+00:00","updated":"2025-01-19T11:17:46.972152+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230087","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230087","title":["AdvGANのclassifierモデルに堅牢性を向上させたモデルを用いた場合の有効性について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"b19d00c3-40e0-43d3-b190-c16993a8105b"},"_deposit":{"id":"230087","pid":{"type":"depid","value":"230087","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"AdvGANのclassifierモデルに堅牢性を向上させたモデルを用いた場合の有効性について","author_link":["619017","619018"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"AdvGANのclassifierモデルに堅牢性を向上させたモデルを用いた場合の有効性について"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岡山理大"},{"subitem_text_value":"岡山理大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/230087/files/IPSJ-Z85-1U-08.pdf","label":"IPSJ-Z85-1U-08.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-1U-08.pdf","filesize":[{"value":"321.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"eee35161-863c-4635-9d98-f19c65ee60b3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"西山, 圭亮"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"島田, 英之"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Deep Neural Networkは入力データに微小な摂動を加えた敵対的サンプルに対して脆弱であることが分かっている。このような敵対的サンプルに対して、Deep Neural Networkは高い確率で誤認識を起こす可能性がある。この敵対的サンプルの生成手法のひとつにGANを利用して生成するAdvGANが提案されており、他の生成手法よりも優れた性能を達成することが示されている。しかし、この実験の欠点は、AdvGANのclassifierモデルの学習データにFGSMより生成された敵対的サンプルしか含まれていないことである。本論文では、classifierモデルに複数の生成手法によって生成された敵対的サンプルを用いて敵対的学習を行い、モデルの堅牢性をより向上させたモデルを用いた場合の有効性について検証を行う。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"594","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"593","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230087,"links":{}}