Abstract
Face recognition has evolved significantly with the advancement of deep learning techniques, enabling its widespread adoption in various applications requiring secure authentication. However, this progress has also increased its exposure to presentation attacks, including face morphing, which poses a serious security threat by allowing one identity to impersonate another. Therefore, modern face recognition systems must be robust against such attacks. In this work, we propose a novel approach for training deep networks for face recognition with enhanced robustness to face morphing attacks. Our method modifies the classification task by introducing a dual-branch classification strategy that effectively handles the ambiguity in the labeling of face morphs. This adaptation allows the model to incorporate morph images into the training process, improving its ability to distinguish them from bona fide samples. Our strategy has been validated on public benchmarks, demonstrating its effectiveness in enhancing robustness against face morphing attacks. Furthermore, our approach is universally applicable and can be integrated into existing face recognition training pipelines to improve classification-based recognition methods.
Abstract (translated)
面部识别技术随着深度学习技术的进步而显著发展，使其在需要安全认证的各种应用中得以广泛应用。然而，这种进步也增加了它对展示攻击（包括人脸融合）的暴露风险，这种攻击允许一个身份冒充另一个身份，从而构成了严重的安全威胁。因此，现代面部识别系统必须具备抵御此类攻击的能力。 在此研究中，我们提出了一种新颖的方法，用于训练深度网络进行面部识别，并增强其对抗面部融合攻击的鲁棒性。我们的方法通过引入双分支分类策略来修改分类任务，这种策略有效地处理了人脸融合标签中的模糊性。这一调整使模型能够将融合图像纳入训练过程，从而提高其区分这些图像与真实样本的能力。 我们的策略已经在公开基准测试中得到了验证，证明它在增强抵御面部融合攻击的鲁棒性方面是有效的。此外，我们的方法具有普遍适用性，并可以整合到现有的面部识别训练流程中，以改进基于分类的方法。
URL
https://arxiv.org/abs/2505.10497
