Reliable Adversarial Distillation with Unreliable Teachers

TL;DR

This paper introduces a new adversarial distillation method called reliable introspective adversarial distillation (IAD), which selectively trusts teacher models based on their reliability across natural and adversarial data, enhancing student robustness.

Contribution

The paper proposes IAD, a novel distillation approach that accounts for teacher unreliability in adversarial settings by dynamically adjusting trust levels based on data type.

Findings

IAD improves adversarial robustness of student models.

Selective trust in teachers enhances distillation effectiveness.

Experimental results show superior performance over traditional methods.

Abstract

In ordinary distillation, student networks are trained with soft labels (SLs) given by pretrained teacher networks, and students are expected to improve upon teachers since SLs are stronger supervision than the original hard labels. However, when considering adversarial robustness, teachers may become unreliable and adversarial distillation may not work: teachers are pretrained on their own adversarial data, and it is too demanding to require that teachers are also good at every adversarial data queried by students. Therefore, in this paper, we propose reliable introspective adversarial distillation (IAD) where students partially instead of fully trust their teachers. Specifically, IAD distinguishes between three cases given a query of a natural data (ND) and the corresponding adversarial data (AD): (a) if a teacher is good at AD, its SL is fully trusted; (b) if a teacher is good at ND but not AD, its SL is partially trusted and the student also takes its own SL into account; (c) otherwise, the student only relies on its own SL. Experiments demonstrate the effectiveness of IAD for improving upon teachers in terms of adversarial robustness.