{"created":"2025-01-19T01:27:53.454705+00:00","updated":"2025-01-19T11:44:04.548865+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00228752","sets":["6164:6165:6462:11379"]},"path":["11379"],"owner":"44499","recid":"228752","title":["難読フォントを用いたCAPTCHAの提案と評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-10-23"},"_buckets":{"deposit":"b1f0fac3-db44-4eff-839c-db8b9b1f5453"},"_deposit":{"id":"228752","pid":{"type":"depid","value":"228752","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"難読フォントを用いたCAPTCHAの提案と評価","author_link":["613728","613729","613727","613730"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"難読フォントを用いたCAPTCHAの提案と評価"},{"subitem_title":"Proposal and evaluation of CAPTCHA using obfuscated fonts","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"CAPTCHA,Gimpy,OCR,機械学習,難読フォント","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":"防衛大学校理工学研究科情報数理専攻"},{"subitem_text_value":"防衛大学校電気情報学群情報工学科"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Master Course Mathematics and Computer Science, Graduate School of Science and Engineering, National Defense Academy of Japan","subitem_text_language":"en"},{"subitem_text_value":" Department of Computer Science, School of Electrical and Computer Engineering, National Defense Academy of Japan","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/228752/files/IPSJ-CSS2023139.pdf","label":"IPSJ-CSS2023139.pdf"},"date":[{"dateType":"Available","dateValue":"2025-10-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2023139.pdf","filesize":[{"value":"2.5 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":"ae0ca0eb-f4ea-4209-a125-b68cc78ba697","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":"高橋, 慧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 康弘"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Akira, Takahashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuhiro, Nakamura","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":"不特定多数のクライアントへサービスを行うウェブアプリでは，クライアントからの通信がプログラムによる自動処理ではなく，人による手動操作であることを確認するため，CAPTCHAが用いられる．中でもテキストベースのCAPTCHAの一種であるGimpyは，作成及び運用が容易であるため，広く活用されている．しかしながら，近年のOCR技術の発展やディープラーニングなどの新しい機械学習アルゴリズムの開発によって，自動処理によりCAPTCHAが突破される危険性が増大している．CAPTCHAによる識別率を向上させるため，人固有の感覚や能力を応用するMulti-model CAPTCHAや，敵対的フィルタを用いた手法などが提案されているが，機械学習を用いた攻撃に対する耐性は定量的に評価されていない．そこで本研究では，CAPTCHAによる識別性能を向上させることを目的に，既存のCAPTCHAの提示する画像に対する機械学習による文字認識精度を評価する．そののち難読フォントを用いたGimpyを作成して正答率を評価することで攻撃耐性が向上したことを示す．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"CAPTCHAs are used in web applications that provide services to an unspecified number of clients to verify that communications from clients are not automatically processed by a program but are manually operated by a human. Gimpy, a text-based CAPTCHA, is widely used because it is easy to create and operate. However, recent advances in OCR technology and the development of new machine learning algorithms such as deep learning have increased the risk of CAPTCHAs being broken through by automated processing, and in order to improve the identification rate of CAPTCHAs, multimodel CAPTCHAs that apply human-specific senses and abilities, and adversarial CAPTCHAs have been developed. CAPTCHAs and methods using adversarial filters have been proposed to improve the identification rate of CAPTCHAs, but their resistance to attacks using machine learning has not been quantitatively evaluated. In this study, we evaluate the accuracy of machine-learning-based character recognition for images presented by existing CAPTCHAs, with the aim of improving the identification performance of CAPTCHAs. We then created a Gimpy using an obfuscated font and evaluated the correct response rate to show that attack resistance has been improved. ","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1024","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":"1020","bibliographicIssueDates":{"bibliographicIssueDate":"2023-10-23","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":228752,"links":{}}