{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:47:00Z","timestamp":1782798420656,"version":"3.54.5"},"reference-count":86,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Gait recognition offers a promising non-intrusive biometric modality, but its widespread adoption is critically hindered by its vulnerability to spoofing attacks, also known as Presentation Attacks (PAs). Developing effective gait anti-spoofing, or Presentation Attack Detection (PAD), mechanisms is therefore paramount for the security and reliability of gait-based authentication systems. While anti-spoofing research in gait has been addressed across both sensor-based (accelerometer\/gyroscope) and vision-based (silhouette\/image) modalities, this work specifically focuses on vision-based, image-level gait PAD, a domain that critically lacks dedicated deep temporal models and standardized benchmarks. Gait spoofing is defined as deliberate manipulation of a subject's external appearance to deceive the authentication system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Unlike prior work, we evaluate models under two practical scenarios: a public-access environment (random splitting) and a restricted-access scenario (LNSOCV). We present a comprehensive comparative study of advanced spatio-temporal architectures for gait anti-spoofing. By repurposing the CASIA-B dataset, we establish a standardized vision-based PAD baseline. We systematically evaluate models incorporating the official Mamba Selective State Space Model (mamba-ssm), a custom Inspired Mamba architecture, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks, all leveraging a robust CNN backbone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our extensive experiments demonstrate that all investigated advanced temporal models significantly improve gait spoofing detection over baseline methods. In open-access environments, the GRU-based model proved to be the most effective for anti-spoofing, reaching a state-of-the-art final validation accuracy of 0.9840 and an ROC-AUC of 0.9983.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Under restricted-access conditions, the LSTM-based model demonstrated the strongest overall performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1821341","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:40:10Z","timestamp":1782798010000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GaitSpoofNet: advanced spatio-temporal architectures for vision-based presentation attack detection"],"prefix":"10.3389","volume":"9","author":[{"given":"Islam","family":"Mohamed","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Informatics, Zagazig University","place":["Zagazig, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Salah","sequence":"additional","affiliation":[{"name":"Department of Computing and Information Sciences, College of Computing and Information Sciences, University of Technology and Applied Sciences","place":["Ibri, Al Dhahirah, Oman"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Essam","family":"Debie","sequence":"additional","affiliation":[{"name":"School of Information Technology and Systems, University of Canberra","place":["Canberra, ACT, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marwa","family":"Abdellah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Informatics, Zagazig University","place":["Zagazig, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amr","family":"Abdellatif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Informatics, Zagazig University","place":["Zagazig, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"264","DOI":"10.3390\/technologies12120264","article-title":"Performance evaluation of various deep learning models in gait recognition using the casia-b dataset","volume":"12","author":"Aman","year":"2024","journal-title":"Technologies"},{"key":"B2","doi-asserted-by":"publisher","first-page":"31949","DOI":"10.1109\/ACCESS.2025.3542837","article-title":"Three-dimensional human model-guided view morphing for clothed gait silhouettes","volume":"13","author":"Ammar Alsherfawi Aljazaerly","year":"2025","journal-title":"IEEE Access"},{"key":"B3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s11760-024-03655-7","article-title":"Feature integration for frontal gait recognition through contour image analysis","volume":"19","author":"Anusha","year":"2024","journal-title":"Signal, Image Video Proc"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.3390\/electronics9071177","article-title":"A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions","volume":"9","author":"Asharf","year":"2020","journal-title":"Electronics"},{"key":"B5","doi-asserted-by":"publisher","first-page":"5123","DOI":"10.32604\/cmc.2023.038120","article-title":"Human gait recognition based on sequential deep learning and best features selection","volume":"75","author":"Avais Hanif","year":"2023","journal-title":"Comp. 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