Adversarial distillation (AD) aims to mitigate deep neural networks' inherent vulnerability to adversarial attacks, thereby providing robust protection for compact models through teacher-student interactions. Despite advancements, existing AD studies still suffer from insufficient robustness due to the limitations of fixed attack strength and attention region shifts. To address these challenges, we propose a strength-adaptive Info-maximizing Adversarial Robustness Distillation paradigm, namely "InfoARD", which strategically incorporates the Attack-Strength Adaptation (ASA) and Mutual-Information Maximization (MIM) to enhance adversarial robustness against adversarial attacks and perturbations. Unlike previous adversarial training (AT) methods that utilize fixed attack strength, the ASA mechanism is designed to capture smoother and generalized classification boundaries by dynamically tailoring the attack strength based on the characteristics of individual instances. Benefiting from mutual information constraints, our MIM strategy ensures the student model effectively learns from various levels of feature representations and attention patterns, thereby deepening the student model's understanding of the teacher model's decision-making processes. Furthermore, a comprehensive multi-granularity distillation is conducted to capture knowledge across multiple dimensions, enabling a more effective transfer of knowledge from the teacher model to the student model. Note that our InfoARD can be seamlessly integrated into existing AD frameworks, further boosting the adversarial robustness of deep learning models. Extensive experiments on various challenging datasets consistently demonstrate the effectiveness and robustness of our InfoARD, surpassing previous state-of-the-art methods.
Liu et al. (Wed,) studied this question.