Hard-Label Based Small Query Black-Box Adversarial Attack

Jeonghwan Park, Paul Miller, Niall McLaughlin; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 3986-3995


We consider the hard-label based black-box adversarial attack setting which solely observes the target model's predicted class. Most of the attack methods in this setting suffer from impractical number of queries required to achieve a successful attack. One approach to tackle this drawback is utilising the adversarial transferability between white-box surrogate models and black-box target model. However, the majority of the methods adopting this approach are soft-label based to take the full advantage of zeroth-order optimisation. Unlike mainstream methods, we propose a new practical setting of hard-label based attack with an optimisation process guided by a pre-trained surrogate model. Experiments show the proposed method significantly improves the query efficiency of the hard-label based black-box attack across various target model architectures. We find the proposed method achieves approximately 5 times higher attack success rate compared to the benchmarks, especially at the small query budgets as 100 and 250.

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@InProceedings{Park_2024_WACV, author = {Park, Jeonghwan and Miller, Paul and McLaughlin, Niall}, title = {Hard-Label Based Small Query Black-Box Adversarial Attack}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2024}, pages = {3986-3995} }