Enhancing adversarial robustness for deep metric learning via neural discrete adversarial training

Due to the security concerns arising from adversarial vulnerability in deep metric learning models, it is essential to enhance their adversarial robustness for secure neural network software development. Existing defense strategies utilize adversarial triplets to enhance adversarial robustness but sacrifice benign performance. This paper proposes a novel framework for enhancing adversarial robustness and maintaining benign performance by introducing the concept of Neural Discrete Adversarial Training (NDAT) for deep metric learning. NDAT employs VQGAN to transform the adversarial triplets into discrete inputs and then minimizes metric loss function on discrete adversarial triplets. NDAT aligns discrete adversarial examples more closely with clean samples, significantly reducing distribution deviation from their clean counterparts. Moreover, the visual explanations reveal that NDAT maintains consistent attention maps between benign and adversarial triplets and concentrates on structure details and object location perturbations. To demonstrate the effectiveness of our approach, we combine NDAT with popular adversarial methods under various perturbation iterations and intensities. Experiment evaluations on three benchmark databases illustrate that our proposed framework for deep metric learning significantly outperforms state-of-the-art defense approaches in terms of both adversarial robustness and benign performance.