统一检测真假人脸攻击，提升识别安全性。
Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection
- 通过成对采样构建真假人脸对比学习框架。
- 在联合攻击数据集上实现2.10%的平均错误率。
- 轻量高效，适合实际部署，适合安全系统研发者。
现代人脸识别系统仍易受物理伪装和数字伪造等欺骗攻击。传统方法分别使用独立模型处理两类攻击，导致系统复杂、延迟高，且难以应对混合攻击。本文提出配对采样对比学习框架，利用自动生成的真伪自拍配对，学习跨模态的活体判别特征。在第六届人脸反欺骗挑战赛统一物理-数字攻击检测基准上，该方法达到2.10%的平均分类错误率（ACER），优于已有方案。模型轻量（4.46 GFLOPs），训练时间不足一小时，便于实际部署。代码与预训练模型已开源：https://github.com/xPONYx/iccv2025_deepfake_challenge。
原文摘要 · Abstract (English)
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.
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