研究人员推出了“Receipt Replay OOD”，一个旨在提高演示攻击检测模型鲁棒性的新基准数据集，特别是针对屏幕重放攻击。该数据集通过使用收据来关注域外（OOD）泛化能力，收据与身份文件具有视觉相似性，但避免了隐私问题。该研究评估了现有模型在领域迁移条件下的性能影响，并将数据集公开发布。 AI
影响 这个新数据集旨在提高用于安全应用的AI模型的泛化能力，特别是用于检测复杂的屏幕重放攻击。
排序理由 该集群描述了为研究目的发布的一篇新学术论文和数据集。
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Public datasets such as DLC-2021, SynID, and KID34K have significantly contributed to research on presentation attack detection for identity documents, including screen replay attacks. However, evaluation of out-of-domain (OOD) robustness remains insufficiently explored, especial…
arXiv:2605.26855v1 Announce Type: new Abstract: Public datasets such as DLC-2021, SynID, and KID34K have significantly contributed to research on presentation attack detection for identity documents, including screen replay attacks. However, evaluation of out-of-domain (OOD) robu…
Public datasets such as DLC-2021, SynID, and KID34K have significantly contributed to research on presentation attack detection for identity documents, including screen replay attacks. However, evaluation of out-of-domain (OOD) robustness remains insufficiently explored, especial…