{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:46Z","timestamp":1758672886386,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Adversarial attacks on Face Recognition (FR) systems have demonstrated significant effectiveness against standalone FR models. However, their practicality diminishes in complete FR systems that incorporate Face Anti-Spoofing (FAS) models, as these models can detect and mitigate a substantial number of adversarial examples. To address this critical yet under-explored challenge, we introduce a novel attack setting that targets both FR and FAS models simultaneously, thereby enhancing the practicability of adversarial attacks on integrated FR systems. Specifically, we propose a new attack method, termed Reference-free Multi-level Alignment (RMA), designed to improve the capacity of black-box attacks on both FR and FAS models. The RMA framework is built upon three key components. Firstly, we propose an Adaptive Gradient Maintenance module to address the imbalances in gradient contributions between FR and FAS models. Secondly, we develop a Reference-free Intermediate Biasing module to improve the transferability of adversarial examples against FAS models. In addition, we introduce a Multi-level Feature Alignment module to reduce feature discrepancies at various levels of representation. Extensive experiments showcase the superiority of our proposed attack method to state-of-the-art adversarial attacks.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/278","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"2494-2502","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models"],"prefix":"10.24963","author":[{"given":"Fengfan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, JLU"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heifei","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuequan","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:35Z","timestamp":1758627215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/278"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/278","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}