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arXiv · 2610.11306

Collaboratively Guided Adversarial Robust Distillation with Teacher-Favorable Examples

Zhi Li·Haowei Liu·Hongchen Yang·Xiaoxuan Wang·Song Gao·Shaowen Yao·Wei Zhou
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Abstract

Adversarial distillation transfers robustness from high-capacity teachers to compact students. Existing adversarial distillation methods mainly use teacher predictions on clean or adversarial examples to supervise student learning. However, teacher-favorable supervision within the perturbation neighborhood remains underexplored in adversarial distillation. We therefore propose Collaboratively Guided Adversarial Robust Distillation (CGARD), which jointly optimizes distinct student-adversarial and teacher-collaborative examples within the same perturbation neighborhood. The teacher-collaborative example is constrained to incur no greater cross-entropy loss under the teacher than the clean input. CGARD combines collaborative teacher guidance with adversarial teacher supervision to improve robust knowledge transfer. Experiments on CIFAR-10 and CIFAR-100, including white-box evaluation and additional black-box transfer evaluation, demonstrate consistent robustness improvements over strong adversarial distillation baselines.

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Zhi Li, Haowei Liu, Hongchen Yang, Xiaoxuan Wang, Song Gao, Shaowen Yao, Wei Zhou. 2026-10-08. Collaboratively Guided Adversarial Robust Distillation with Teacher-Favorable Examples. https://arxiv.org/abs/2610.11306

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