Abstract
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 this https URL.
Abstract (translated)
现代面部识别系统仍然容易受到模拟攻击，包括物理展示攻击和数字伪造。传统上，这两种攻击向量分别由各自的模型处理，每个模型都针对特定的特征和模式。然而，维护独立的检测器会增加系统的复杂性和推理延迟，并使系统暴露于结合了两种攻击向量的风险之下。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自拍照与模拟攻击图像对来学习不受模态限制的生命体征提示符。在第六届面部防伪造挑战赛（Unified Physical-Digital Attack Detection）基准测试中，我们的方法实现了2.10%的平均分类错误率(ACER)，优于以前的方法。该框架轻量级（4.46 GFLOPs），训练时间不到一小时，在实际部署中具有实用性。代码和预训练模型可在此链接获取：[原链接中的URL]
URL
https://arxiv.org/abs/2508.14980
