Abstract
Face recognition systems are robust against environmental changes and noise, and thus may be vulnerable to illegal authentication attempts using user face photos, such as spoofing attacks. To prevent such spoofing attacks, it is crucial to discriminate whether the input image is a live user image or a spoofed image prior to the face recognition process. Most existing spoofing attack detection methods utilize deep learning, which necessitates a substantial amount of training data. Consequently, if malicious data is injected into a portion of the training dataset, a specific spoofing attack may be erroneously classified as live, leading to false this http URL this paper, we propose a novel backdoor poisoning attack method to demonstrate the latent threat of backdoor poisoning within face anti-spoofing detection. The proposed method enables certain spoofing attacks to bypass detection by embedding features extracted from the spoofing attack's face image into a live face image without inducing any perceptible visual this http URL experiments conducted on public datasets, we demonstrate that the proposed method constitutes a realistic threat to existing spoofing attack detection systems.
Abstract (translated)
面部识别系统在面对环境变化和噪音时具有较强的鲁棒性，但这使得它们可能容易受到使用用户面部照片进行的非法认证尝试（如伪装攻击）的影响。为了防止这类伪装攻击，在执行面部识别之前必须区分输入图像是否为真实用户的活体图像或伪造图像。现有的大多数防伪检测方法都依赖于深度学习技术，这需要大量的训练数据。因此，如果恶意数据被注入到部分训练集中，可能会导致特定的伪装攻击错误地被分类为活体，从而引发误报。 本文提出了一种新颖的后门投毒攻击方法，展示了面部防伪检测中潜在的后门投毒威胁。该方法通过将来自伪造图像的特征嵌入到真实用户的活体图像中而不引起任何可察觉的视觉变化，使某些伪装攻击能够绕过检测。实验结果表明，在公共数据集上使用这种方法会对现有的防伪攻击检测系统构成切实的威胁。
URL
https://arxiv.org/abs/2509.03108
