构建小规模跨域屏幕重放检测基准，验证模型在真实场景下的泛化能力。
Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift
- 基于收据设计跨域测试集，模拟真实文档的视觉特征与磨损特性。
- 在跨域条件下测试模型，发现域偏移显著降低检测准确率。
- 适合研究身份文件防伪中鲁棒性与泛化性能的学者使用。
公开数据集如DLC-2021、SynID和KID34K对身份文件的展示攻击检测研究有重要推动作用，包括屏幕重放攻击。然而，域外（OOD）鲁棒性的评估仍不充分，尤其在真实域偏移场景下。本文提出Receipt Replay OOD，一个用于屏幕重放检测的小型域外基准。收据与身份文件共享平面几何、弯曲边缘、磨损痕迹及文字或标志图案等特征，同时避免个人身份信息限制。我们在跨域条件下评估文档重放检测模型，揭示域偏移对泛化性能的影响。该数据集已公开可用。
原文摘要 · Abstract (English)
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, especially under realistic domain shifts. In this work, we introduce Receipt Replay OOD, a small out-of-domain benchmark for screen replay detection. Receipts share several characteristics with identity documents, including planar geometry, curved corners, wear-and-tear artifacts, and text or logo patterns, while avoiding personally identifiable information constraints commonly associated with identity documents. We evaluate document replay detection models under cross-domain conditions and demonstrate the impact of domain shift on generalization performance. The dataset is publicly available.
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