Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples
This addresses the vulnerability of neural networks to adversarial attacks in black-box settings, offering a provable method to reduce query costs, which is incremental as it builds on existing knowledge distillation techniques.
The paper tackles the problem of high query costs in black-box adversarial attacks by using knowledge distillation to train a surrogate model iteratively on an expanding dataset, proving that with sufficient learning capabilities, an adversarial example can be guaranteed within a finite number of iterations.
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.