Update Publication.md

Author: Leminhbinh0209

Publication.md

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 <h4 style="margin-top:40px"><b>2025</b></h4>
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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>
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+                        <p style="margin-top: 0px;margin-bottom: 0px;text-align: justify;margin-left: 20px;"> <small><a href="https://arxiv.org/pdf/2401.04364"> <img  alt="paper" src="paper_grey.svg" style="right: 15px; position:relative; top:5px; width: 18px; height: 18px;"> </a>  &nbsp;&nbsp;   <a href="https://www.abc.net.au/listen/programs/radionational-breakfast/csiro-deepfake-research-ahead-of-federal-election/105044852"> [News] </a></small> </p>
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