Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916049492901888 |
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| author | Vinogradov, Alexander |
| author_facet | Vinogradov, Alexander |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_26855 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift Vinogradov, Alexander Computer Vision and Pattern Recognition 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. |
| title | Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.26855 |