As an emerging biometric technology, multi-spectral palmprint recognition has attracted increasing attention in security due to its high accuracy and ease of use. Compared to single spectral case, multi-spectral palmprint model is more susceptible to the attack of adversarial examples. However, the previous adversarial example attack approaches cannot generate the most aggressive adversarial examples for multi-spectral palmprint recognition. In addition, most of them are dependent on the explicit architecture or need time-consuming queries about the network to be attacked, which significantly limits their application in the field of security. To solve the above problems, in this paper, we proposed the multi-spectral palmprints joint attack and defense framework based on multi-view adversarial examples learning. First, we respectively capture the multi-view deep common feature space for the different spectra and the discriminative feature space across the different subjects. Second, we introduce perturbation in the deep common space to achieve adversarial multi-spectral palmprints with gradient propagation. In addition, we pursue the manifold of the difference space and use it to suppress the discriminability of the recognition model with adversarial region theory. Finally, the generated adversarial examples are fed into the training model to enhance the robustness of the recognition algorithm. The experimental results on multi-spectral palmprint dataset demonstrate that the proposed multi-view joint attack approach is superior to the state-of-the-art adversarial example attack methods in attack accuracy and transferability. Moreover, the defense strategy with the adversarial examples by our method can significantly promote the robustness of multi-spectral palmprint recognition methods.

中文翻译：

---

多光谱掌纹联合攻防与对抗样本学习


多光谱掌纹识别作为一种新兴的生物识别技术，因其准确率高、使用方便等特点，在安全领域受到越来越多的关注。与单光谱情况相比，多光谱掌纹模型更容易受到对抗性例子的攻击。然而，以前的对抗性示例攻击方法无法为多光谱掌纹识别生成最具攻击性的对抗性示例。此外，它们大多数依赖于显式架构或需要耗时地查询要攻击的网络，这极大地限制了它们在安全领域的应用。针对上述问题，本文提出了基于多视角对抗实例学习的多光谱掌纹联合攻防框架。首先，我们分别捕获不同光谱的多视图深层共同特征空间和不同主题的判别特征空间。其次，我们在深层公共空间中引入扰动，以通过梯度传播实现对抗性多光谱掌纹。此外，我们追求差异空间的流形，并用它来抑制对抗区域理论的识别模型的可辨别性。最后，将生成的对抗样本输入到训练模型中，以增强识别算法的鲁棒性。多光谱掌纹数据集上的实验结果表明，所提出的多视图联合攻击方法在攻击准确性和可转移性方面优于最先进的对抗性示例攻击方法。 此外，我们的方法的对抗性例子的防御策略可以显着提高多光谱掌纹识别方法的鲁棒性。