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Data-free knowledge distillation (KD) helps transfer knowledge from apre-trained model (known as the teacher model) to a smaller model (known as thestudent model) without access to the original training data used for trainingthe teacher model. However, the security of the synthetic orout-of-distribution (OOD) data required in data-free KD is largely unknown andunder-explored. In this work, we make the first effort to uncover the securityrisk of data-free KD w.r.t. untrusted pre-trained models. We then proposeAnti-Backdoor Data-Free KD (ABD), the first plug-in defensive method fordata-free KD methods to mitigate the chance of potential backdoors beingtransferred. We empirically evaluate the effectiveness of our proposed ABD indiminishing transferred backdoor knowledge while maintaining compatibledownstream performances as the vanilla KD. We envision this work as a milestonefor alarming and mitigating the potential backdoors in data-free KD. Codes arereleased at https://github.com/illidanlab/ABD.

Revisiting Data-Free Knowledge Distillation with Poisoned Teachers