Explanation-Guided Adversarial Example Attacks

doi:10.1016/j.bdr.2024.100451

Explanation-Guided Adversarial Example Attacks

2024-05-01

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AI

DOI：10.1016/j.bdr.2024.100451

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Neural network -based classifiers are vulnerable to adversarial example attacks even in a black -box setting. Existing adversarial example generation technologies mainly rely on optimization -based attacks, which optimize the objective function by iterative input perturbation. While being able to craft adversarial examples, these techniques require big budgets. Latest transfer -based attacks, though being limited queries, also have a disadvantage of low attack success rate. In this paper, we propose an adversarial example attack method called MEAttack using the model -agnostic explanation technology, which can more efficiently generate adversarial examples in the black -box setting with limited queries. The core idea is to design a novel model -agnostic explanation method for target models, and generate adversarial examples based on model explanations. We experimentally demonstrate that MEAttack outperforms the state-of-the-art attack technology, i.e., AutoZOOM. The success rate of MEAttack is 4.54%-47.42% higher than AutoZOOM, and its query efficiency is reduced by 2.6-4.2 times. Experimental results show that MEAttack is efficient in terms of both attack success rate and query efficiency.

Keyword:

Deep neural network

Model explanation

Adversarial examples

Black-box

Label-only

Explanation-Guided Adversarial Example Attacks

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