{"links":{},"id":238547,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00238547","sets":["1164:3616:11475:11763"]},"path":["11763"],"owner":"44499","recid":"238547","title":["敵対的攻撃に対する堅牢性向上のための適応的マルチチャネル選択法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-08-28"},"_buckets":{"deposit":"52e9c179-eb2a-4118-807e-92c974bffd41"},"_deposit":{"id":"238547","pid":{"type":"depid","value":"238547","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"敵対的攻撃に対する堅牢性向上のための適応的マルチチャネル選択法","author_link":["653270","653271","653268","653269"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"敵対的攻撃に対する堅牢性向上のための適応的マルチチャネル選択法"},{"subitem_title":"Adaptive multi-channel selection method for enhancing robustness against adversarial attacks","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-08-28","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工科大学"},{"subitem_text_value":"東京工科大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo University of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo University of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/238547/files/IPSJ-AVM24125012.pdf","label":"IPSJ-AVM24125012.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AVM24125012.pdf","filesize":[{"value":"2.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"27"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"ffd64d9c-3b82-4011-925c-2f247b410004","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松井, 清修"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"青木, 輝勝"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Seishu, Matsui","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Terumasa, Aoki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438399","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8582","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，深層学習モデルの敵対的攻撃に対する新しい防御手法「適応的マルチチャネル選択法（AMCS）」を提案する．AMCS は，小さいカーネルと大きいカーネルの畳み込み層を組み合わせ，入力層で特定のチャネルを適応的に選択する．この構造により，モデルは多様な特徴を抽出し，頑健性を向上させることができる．実験では，画像分類タスクにおいて通常画像に対する高精度を維持しつつ，ブラックボックス攻撃に対して優れた堅牢性を示した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this study, we present a novel defense method against adversarial attacks on deep learning models, called the Adaptive Multi-Channel Selection Method (AMCS). AMCS combines convolutional layers with small and large kernels to adaptively select specific channels in the input layer. This structure allows the model to extract diverse features, thereby enhancing its robustness. Experiments demonstrated that AMCS maintains high accuracy on normal images while exhibiting superior robustness against black-box attacks in image classification tasks.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告オーディオビジュアル複合情報処理（AVM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-08-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"2024-AVM-125"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:41:50.592654+00:00","updated":"2025-01-19T08:31:59.009350+00:00"}