Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913999058108416 |
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| author | Balykin, Andrei Ganiev, Anvar Kondranin, Denis Polevoda, Kirill Liudkevich, Nikolai Petrov, Artem |
| author_facet | Balykin, Andrei Ganiev, Anvar Kondranin, Denis Polevoda, Kirill Liudkevich, Nikolai Petrov, Artem |
| contents | Modern face recognition systems remain vulnerable to spoofing attempts, including both physical presentation attacks and digital forgeries. Traditionally, these two attack vectors have been handled by separate models, each targeting its own artifacts and modalities. However, maintaining distinct detectors increases system complexity and inference latency and leaves systems exposed to combined attack vectors. We propose the Paired-Sampling Contrastive Framework, a unified training approach that leverages automatically matched pairs of genuine and attack selfies to learn modality-agnostic liveness cues. Evaluated on the 6th Face Anti-Spoofing Challenge Unified Physical-Digital Attack Detection benchmark, our method achieves an average classification error rate (ACER) of 2.10 percent, outperforming prior solutions. The framework is lightweight (4.46 GFLOPs) and trains in under one hour, making it practical for real-world deployment. Code and pretrained models are available at https://github.com/xPONYx/iccv2025_deepfake_challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection Balykin, Andrei Ganiev, Anvar Kondranin, Denis Polevoda, Kirill Liudkevich, Nikolai Petrov, Artem Computer Vision and Pattern Recognition Modern face recognition systems remain vulnerable to spoofing attempts, including both physical presentation attacks and digital forgeries. Traditionally, these two attack vectors have been handled by separate models, each targeting its own artifacts and modalities. However, maintaining distinct detectors increases system complexity and inference latency and leaves systems exposed to combined attack vectors. We propose the Paired-Sampling Contrastive Framework, a unified training approach that leverages automatically matched pairs of genuine and attack selfies to learn modality-agnostic liveness cues. Evaluated on the 6th Face Anti-Spoofing Challenge Unified Physical-Digital Attack Detection benchmark, our method achieves an average classification error rate (ACER) of 2.10 percent, outperforming prior solutions. The framework is lightweight (4.46 GFLOPs) and trains in under one hour, making it practical for real-world deployment. Code and pretrained models are available at https://github.com/xPONYx/iccv2025_deepfake_challenge. |
| title | Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.14980 |