{"created":"2025-01-19T01:29:54.944591+00:00","updated":"2025-01-19T11:13:22.706461+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230263","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230263","title":["画像特徴量のロバスト性に着目した教師なし敵対的サンプル検知"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"39835d72-69e5-4345-9bd6-e40bb759712f"},"_deposit":{"id":"230263","pid":{"type":"depid","value":"230263","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"画像特徴量のロバスト性に着目した教師なし敵対的サンプル検知","author_link":["619505","619506"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"画像特徴量のロバスト性に着目した教師なし敵対的サンプル検知"}]},"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":"同志社大"},{"subitem_text_value":"同志社大"}]},"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/230263/files/IPSJ-Z85-6ZM-01.pdf","label":"IPSJ-Z85-6ZM-01.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-6ZM-01.pdf","filesize":[{"value":"294.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"01968fd5-7e58-4f20-9fc6-cd070b854764","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":"神田, 悠斗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"波多野, 賢治"}],"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":"敵対的サンプルとは，機械学習モデルが誤分類を引き起こすよう巧妙に細工された入力データである．そのような入力を含むデータ群に対して誤分類を防ぐためには，敵対的サンプルを検知する必要がある．近年の研究では，機械学習モデルが予測の根拠としている特徴量のロバスト性に着目し，特にロバスト性の低い特徴量の存在が敵対的サンプルを生み出す主要な原因であると主張している．この仮説に従い，本研究では入力画像特徴量のロバスト性に着目した敵対的サンプル検知手法を提案する．具体的には，入力画像に対してノイズを付与することで特徴量のロバスト性を計測し，正常な画像と敵対的サンプルとの差異を敵対的サンプルの検知に利用する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"956","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"955","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230263,"links":{}}