Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples
black-box adversarial attack;knowledge distillation;transferable adversarial attack
violating format guideline (reduced margin)
Submission Desk Rejected by Program Chairs
A method to provably compute an adversarial example for a black-box classification neural network
@misc{
lukyanov2024model,
title={Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples},
author={Kirill Lukyanov and Andrew Perminov and Denis Turdakov and Mikhail Pautov},
year={2024},
url={https://openreview.net/forum?id=SY70rVSr3M}
}
The vulnerability of artificial neural networks to adversarial perturbations in the black-box setting is widely studied in the literature. The majority of attack methods to construct these perturbations suffer from an impractically large number of queries required to find an adversarial example. In this work, we focus on knowledge distillation as an approach to conduct transfer-based black-box adversarial attacks and propose an iterative training of the surrogate model on an expanding dataset. This work is the first, to our knowledge, to provide provable guarantees on the success of knowledge distillation-based attack on classification neural networks: we prove that if the student model has enough learning capabilities, the attack on the teacher model is guaranteed to be found within the finite number of distillation iterations.
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~Kirill_Lukyanov1 ~Andrew_Perminov1 ~Denis_Turdakov1 ~Mikhail_Pautov1
Kirill Lukyanov Andrew Perminov Denis Turdakov Mikhail Pautov
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black-box adversarial attack knowledge distillation transferable adversarial attack
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lukyanov|model_mimic_attack_knowledge_distillation_for_provably_transferable_adversarial_examples
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Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission