arXiv Computer Vision
Oct 9

Collaboratively Guided Adversarial Robust Distillation with Teacher-Favorable Examples

Collaboratively Guided Adversarial Robust Distillation (CGARD) is a method that transfers robustness from high‑capacity teachers to compact students by jointly optimizing student‑adversarial and teacher‑collaborative examples within the same perturbation neighborhood. The teacher‑collaborative example is constrained so that the teacher’s cross‑entropy loss does not exceed that of the clean input, combining collaborative guidance with adversarial supervision. Experiments on CIFAR‑10 and CIFAR‑100, including white‑box and black‑box evaluations, show consistent robustness improvements over strong adversarial distillation baselines.

By Zhi Li, Haowei Liu, Hongchen Yang, Xiaoxuan Wang, Song Gao, Shaowen Yao, Wei Zhou
arXiv AI
Sep 10

How to Backdoor Image Knowledge Distillation

The paper demonstrates that image knowledge distillation can be backdoored even when the teacher model is clean, by poisoning the distillation dataset with triggered and manipulated images that the teacher already classifies as a target label. The attack, effective at poisoning rates as low as 10%, uses targeted adversarial perturbations and GAN-based class transitions to embed a backdoor into the student model while preserving its performance on clean data. The study highlights that the security of knowledge distillation depends not only on the teacher but also on the integrity of the distillation data.

By Qian Ma, Chen Wu, Prasenjit Mitra, Sencun Zhu