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arXiv 提交日期: 2026-05-26
📄 Abstract - Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift

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.

顶级标签: computer vision benchmark model evaluation
详细标签: screen replay detection domain shift out-of-domain robustness presentation attack detection 或 搜索:

收据重放OOD:一个用于屏幕重放检测领域迁移的小型基准测试集 / Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift


1️⃣ 一句话总结

本文提出了一个名为“收据重放OOD”的小型基准数据集,用于评估屏幕重放攻击检测模型在面对不同文档类型(从身份证件切换到收据)时的泛化能力,并揭示了领域迁移会显著降低模型性能这一关键问题。

源自 arXiv: 2605.26855