中文标题#
成对采样对比框架用于联合物理 - 数字人脸攻击检测
英文标题#
Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection
中文摘要#
现代人脸识别系统仍然容易受到欺骗尝试的攻击，包括物理呈现攻击和数字伪造。 传统上，这两种攻击向量分别由单独的模型处理，每个模型针对其自身的伪影和模态。 然而，维护不同的检测器会增加系统复杂性和推理延迟，并使系统暴露于组合攻击向量下。 我们提出了配对采样对比框架，这是一种统一的训练方法，利用自动匹配的真实和攻击自拍图像对来学习与模态无关的活体提示。 在第六届人脸识别反欺骗挑战统一物理 - 数字攻击检测基准上评估，我们的方法实现了平均分类错误率（ACER）为 2.10%，优于之前的方法。 该框架轻量级（4.46 GFLOPs），并在一小时内完成训练，使其适用于实际部署。 代码和预训练模型可在 https://github.com/xPONYx/iccv2025_deepfake_challenge 获取。
英文摘要#
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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