.png){kind=icon}
Return
Explanation-Guided Adversarial Example Attacks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bdr.2024.100451.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Deep neural network
Model explanation
Adversarial examples
Black-box
Label-only
Journal
IF:
4.2
Papers:
416
Citations:
1.1K
.png){kind=icon}
