Decision-Based Query Efficient Adversarial Attack via Adaptive Boundary Learning

doi:10.1109/TDSC.2023.3289298

Decision-Based Query Efficient Adversarial Attack via Adaptive Boundary Learning

2024-07-01

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DOI：10.1109/TDSC.2023.3289298

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Decision-based adversarial attacks pose a severe threat to real-world applications of Deep Neural Networks (DNNs), as attackers are assumed to have no prior knowledge about target model except hard labels of model outputs. Existing decision-based attacks require a large number of queries on the target model for a successful attack. In this article, we propose DEAL, a decision-based query efficient adversarial attack based on adaptive boundary learning. DEAL relies on a local model initialized through meta-learning mechanism to obtain the ability to fit new decision boundaries. We conduct extensive experiments to evaluate the effectiveness of DEAL, which demonstrates that it outperforms 8 state-of-the-art attacks. Specifically for the evaluation on CIFAR-10 dataset, DEAL achieves similar attack success rates with a maximum query reduction of 51% in untargeted attacks and 14% in targeted attacks, respectively.

Keyword:

Adaptation models

Perturbation methods

Optimization

Training

Task analysis

Predictive models

Metalearning

Adversarial attack

black-box attack

decision-based

meta-learning

query efficiency

Decision-Based Query Efficient Adversarial Attack via Adaptive Boundary Learning

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