{"created":"2025-01-19T01:30:22.108926+00:00","updated":"2025-01-19T11:06:23.836566+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230538","sets":["6504:11436:11442"]},"path":["11442"],"owner":"44499","recid":"230538","title":["複数の機械学習モデルに適用可能な電子透かし手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"5e622451-8419-47ed-b2f6-915b7599b6c0"},"_deposit":{"id":"230538","pid":{"type":"depid","value":"230538","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"複数の機械学習モデルに適用可能な電子透かし手法の検討","author_link":["621258","621259","621260"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"複数の機械学習モデルに適用可能な電子透かし手法の検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セキュリティ","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":"秋田県大"},{"subitem_text_value":"秋田県大"},{"subitem_text_value":"秋田県大"}]},"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/230538/files/IPSJ-Z85-4ZD-03.pdf","label":"IPSJ-Z85-4ZD-03.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-4ZD-03.pdf","filesize":[{"value":"926.0 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"d93663a5-e99b-46a7-bb8b-4e12d6390209","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":"中林, 舜葵"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"平間, 海渡"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"光澤, 敦"}],"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":"機械学習モデルは常に盗難の危機にさらされている．このような問題に対してマルチメディアの分野で利用されている電子透かしを機械学習へと適用することでモデルの所有権を主張する研究が盛んに行われている．電子透かしに関する最近の研究は画像分類タスクを対象としたものが主であり，モデルの構造としてはCNNが用いられることが多い．しかし、画像分類タスクに限っても、Vision Transfomer (ViT)を始めとするCNN以外のモデルが登場しており、それらに関する電子透かしの検討が必要である．本研究では、既存の電子透かし手法がViTなどのCNN以外の機械学習モデルにおいてもこれまでと同様に適用できるかについて、透かしの精度と透かしを除去する攻撃に対して堅牢性を評価することにより検証を行う．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"516","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"515","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230538,"links":{}}