{"created":"2025-01-19T01:27:54.778306+00:00","updated":"2025-01-19T11:43:25.592540+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00228775","sets":["6164:6165:6462:11379"]},"path":["11379"],"owner":"44499","recid":"228775","title":["モデル抽出攻撃の新たな評価指標に向けた決定境界の可視化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-10-23"},"_buckets":{"deposit":"4e1829a5-c60a-4cc0-98e2-0c3cf65cad0d"},"_deposit":{"id":"228775","pid":{"type":"depid","value":"228775","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"モデル抽出攻撃の新たな評価指標に向けた決定境界の可視化","author_link":["613865","613868","613867","613866","613870","613869"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"モデル抽出攻撃の新たな評価指標に向けた決定境界の可視化"},{"subitem_title":"Visualizing Decision Boundary for Model Extraction Attacks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"モデル抽出攻撃　機械学習　決定境界　可視化","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2023-10-23","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学"},{"subitem_text_value":"大阪大学"},{"subitem_text_value":"富士通株式会社"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"OSAKA UNIVERSITY","subitem_text_language":"en"},{"subitem_text_value":"OSAKA UNIVERSITY","subitem_text_language":"en"},{"subitem_text_value":"FUJITSU Limited","subitem_text_language":"en"}]},"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/228775/files/IPSJ-CSS2023162.pdf","label":"IPSJ-CSS2023162.pdf"},"date":[{"dateType":"Available","dateValue":"2025-10-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2023162.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"3dc99561-5a18-42b5-ade0-03f95c35730a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"武内, 祐哉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"矢内, 直人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森川, 郁也"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuya, Takeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yanai, Naoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ikuya, Morikawa","creatorNameLang":"en"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"モデル抽出攻撃は攻撃対象となるモデルの出力を攻撃者が自らのモデルで学習することで，同等の推論精度を持つ抽出モデルを構築する攻撃である．本稿では，モデル抽出攻撃における新たな評価指標として，決定境界の可視化に着目するとともに，その結果を定量評価できる指標として決定境界類似性を提案する．大まかには，決定境界類似性は被害者モデルと抽出モデルの決定境界をそれぞれ可視化することで，画像としての類似度を比較する．具体的な評価手法として決定境界の可視化に COFFI (Sohns ら, Comp. Graph. For., 2023)，類似度の比較に LPIPS (Zhang ら, CVPR 2018) を用いて代表的な攻撃方法を評価したところ，二つの知見を得た．一つ目は，既存の評価指標である忠実度では表現できない知見として，決定境界を可視化したことにより，敵対的サンプルを用いる攻撃においては抽出モデルは少ないクエリでも決定境界を効果的に構成できていることを確認した．二つ目は，決定境界類似性において可視化の手法を変えることで，忠実度を詳細化した分析も可能となることである．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A model extraction attack is an attack where an adversary obtains a substitute model with equivalent accuracy by learning the output of the victim’s model. In this paper, we focus on visualization of decision boundaries and then propose a new evaluation metric for model extraction attacks called decision boundary similarity. It is a metric whereby the decision boundaries of a victim model and those of a substitute model are evaluated by visualizing and comparing them. When we use COFFI as a visualization of decision boundaries and LPIPS as a similarity for several well-known attacks, we found two insights. The first insight is a substitute model by an existing attack based on adversarial examples can obtain decision boundaries even for a few queries. The second insight is that the decision boundary similarity can also represent fidelity, which is an existing metric, using a different visualization method.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1194","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2023論文集"}],"bibliographicPageStart":"1187","bibliographicIssueDates":{"bibliographicIssueDate":"2023-10-23","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":228775,"links":{}}