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arXiv · 2507.21992

Teach Me to Trick: Exploring Adversarial Transferability via Knowledge Distillation

Siddhartha Pradhan·Shikshya Shiwakoti·Neha Bathuri
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Abstract

We investigate whether knowledge distillation (KD) from multiple heterogeneous teacher models can enhance the generation of transferable adversarial examples. A lightweight student model is trained using two KD strategies: curriculum-based switching and joint optimization, with ResNet50 and DenseNet-161 as teachers. The trained student is then used to generate adversarial examples using FG, FGS, and PGD attacks, which are evaluated against a black-box target model (GoogLeNet). Our results show that student models distilled from multiple teachers achieve attack success rates comparable to ensemble-based baselines, while reducing adversarial example generation time by up to a factor of six. An ablation study further reveals that lower temperature settings and the inclusion of hard-label supervision significantly enhance transferability. These findings suggest that KD can serve not only as a model compression technique but also as a powerful tool for improving the efficiency and effectiveness of black-box adversarial attacks.

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Siddhartha Pradhan, Shikshya Shiwakoti, Neha Bathuri. 2025-07-29. Teach Me to Trick: Exploring Adversarial Transferability via Knowledge Distillation. https://arxiv.org/abs/2507.21992

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