Weakly Supervised Training for Hologram Verification in Identity Documents
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arXiv
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| Format: | Preprint |
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2024
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| author | Pouliquen, Glen Chiron, Guillaume Chazalon, Joseph Géraud, Thierry Awal, Ahmad Montaser |
| author_facet | Pouliquen, Glen Chiron, Guillaume Chazalon, Joseph Géraud, Thierry Awal, Ahmad Montaser |
| contents | We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on MIDV-HOLO, while maintaining a high recall on documents from MIDV-2020 used as attack samples. It is also the first method, to date, to effectively address the photo replacement attack task, and can be trained on either genuine samples, attack samples, or both for increased performance. By enabling to verify OVD shapes and dynamics with very little supervision, this work opens the way towards the use of massive amounts of unlabeled data to build robust remote identity document verification systems on commodity smartphones. Code is available at https://github.com/EPITAResearchLab/pouliquen.24.icdar |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17253 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weakly Supervised Training for Hologram Verification in Identity Documents Pouliquen, Glen Chiron, Guillaume Chazalon, Joseph Géraud, Thierry Awal, Ahmad Montaser Computer Vision and Pattern Recognition We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on MIDV-HOLO, while maintaining a high recall on documents from MIDV-2020 used as attack samples. It is also the first method, to date, to effectively address the photo replacement attack task, and can be trained on either genuine samples, attack samples, or both for increased performance. By enabling to verify OVD shapes and dynamics with very little supervision, this work opens the way towards the use of massive amounts of unlabeled data to build robust remote identity document verification systems on commodity smartphones. Code is available at https://github.com/EPITAResearchLab/pouliquen.24.icdar |
| title | Weakly Supervised Training for Hologram Verification in Identity Documents |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.17253 |