Update Publication.md

Author: think24

Publication.md

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                         <p style="margin-top: 0px;margin-bottom: 0px;text-align: justify;"> <small>  Binh M. Le, Jiwon Kim, <i><b>Simon S. Woo*</b> </i>, Shahroz Tariq, Alsharif Abuadbba, and Kristen Moore</small> </p>
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-                        <p style="margin-top: 0px;margin-bottom: 0px;text-align: justify;"> <small> <b><a href="https://eurosp2025.ieee-security.org/">10th IEEE European Symposium on Security and Privacy, Venice, July, 2025 </a> </b> </small> </p>
+                        <p style="margin-top: 0px;margin-bottom: 0px;text-align: justify;"> <small> <b><a href="https://eurosp2025.ieee-security.org/">10th IEEE European Symposium on Security and Privacy (Euro S&P), Venice, July, 2025 </a> </b> </small> </p>
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                         <p style="margin-top: 0px;margin-bottom: 0px;text-align: justify;"><small>This paper extensively reviews and analyzes state-of-the-art deepfake detectors, evaluating them against several critical criteria. These criteria categorize detectors into 4 high-level groups and 13 fine-grained sub-groups, aligned with a unified conceptual framework we propose. This classification offers practical insights into the factors affecting detector efficacy. We evaluate the generalizability of 16 leading detectors across comprehensive attack scenarios, including black-box, white-box, and gray-box settings. Our systematized analysis and experiments provide a deeper understanding of deepfake detectors and their generalizability, paving the way for future research and the development of more proactive defenses against deepfakes.</small> </p>