PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor (2403.06668v3)

Abstract: Adversarial robustness of the neural network is a significant concern when it is applied to security-critical domains. In this situation, adversarial distillation is a promising option which aims to distill the robustness of the teacher network to improve the robustness of a small student network. Previous works pretrain the teacher network to make it robust against the adversarial examples aimed at itself. However, the adversarial examples are dependent on the parameters of the target network. The fixed teacher network inevitably degrades its robustness against the unseen transferred adversarial examples which target the parameters of the student network in the adversarial distillation process. We propose PeerAiD to make a peer network learn the adversarial examples of the student network instead of adversarial examples aimed at itself. PeerAiD is an adversarial distillation that trains the peer network and the student network simultaneously in order to specialize the peer network for defending the student network. We observe that such peer networks surpass the robustness of the pretrained robust teacher model against adversarial examples aimed at the student network. With this peer network and adversarial distillation, PeerAiD achieves significantly higher robustness of the student network with AutoAttack (AA) accuracy by up to 1.66%p and improves the natural accuracy of the student network by up to 4.72%p with ResNet-18 on TinyImageNet dataset. Code is available at https://github.com/jaewonalive/PeerAiD.

• The paper introduces a novel peer tutoring method that addresses the flaw in transferring adversarial robustness from teacher to student networks.
• It employs concurrent training of a peer network using adversarial examples from the student, achieving up to 1.66% AA and 4.72% natural accuracy improvements.
• This approach challenges conventional adversarial distillation methods and offers a practical solution for robust model deployment in security-critical domains.

Analysis of PeerAiD: A Novel Approach to Adversarial Distillation

The research paper titled "PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor" presents a novel methodology designed to enhance the adversarial robustness of neural networks, specifically targeting security-critical domains. The authors identify a critical flaw in existing methods of adversarial distillation, where the robustness of a pre-trained teacher network fails against adversarial examples transferred to a student network. This work introduces the PeerAiD model, which utilizes a peer network trained alongside the student network, offering an innovative approach to adversarial distillation.

Adversarial distillation is a process aimed at strengthening the robustness of a smaller student neural network by transferring knowledge from a larger, pre-trained teacher network. While existing methods rely on a robust teacher model to guide the student during training, these teacher models do poorly in handling adversarial examples targeting the student model. PeerAiD proposes an alternative strategy where a peer network is trained concurrently with the student network using adversarial examples generated by the student. This peer network does not attempt to solve the same adversarial attacks targeting the teacher itself but specializes in mitigating those targeting the student, resulting in increased robustness.

Key results displayed in this paper highlight PeerAiD's capability to enhance the robust accuracy of student networks with significant improvements. Notably, PeerAiD delivers up to a 1.66% increase in AutoAttack (AA) accuracy and a 4.72% improvement in natural accuracy over existing methods using architectures like ResNet-18 on datasets such as TinyImageNet. This result underlines the success of the peer network in providing more relevant, less degraded guidance during adversarial distillation than the traditional robust teacher networks.

The implications of this paper extend to both practical and theoretical domains. Practically, this approach offers a viable path to train robust models that are deployable in environments where security is crucial, such as autonomous vehicles or sensitive data systems. Theoretically, it challenges the prevailing assumptions about the transferability of adversarial robustness from a teacher to a student network, highlighting the limitations of adversarial examples in effectively training robust student networks.

While PeerAiD has demonstrated promising results, future developments may include exploring the scalability of this method to even larger neural architectures or evaluating PeerAiD under diverse adversarial threat models. Furthermore, understanding the theoretical constructs underlying the specialized peer network's success could provide deeper insights into adversarial robustness.

This paper presents an insightful step forward in adversarial machine learning. By identifying and addressing discrepancies in adversarial example management between teacher and student networks, PeerAiD lays the groundwork for more resilient and adaptable AI systems. This advancement not only promises enhanced practicality but also encourages ongoing research into the nuanced mechanisms of adversarial learning.