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Projection & Probability-Driven Black-Box Attack
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer from the need for excessive queries, as it is non-trivial to find an appropriate direction to optimize in the highdimensional space. In this paper, we propose Projection & Probability-driven Black-box Attack (PPBA) to tackle this problem by reducing the solution space and providing better optimization. For reducing the
doi:10.1109/cvpr42600.2020.00044
dblp:conf/cvpr/LiJ0LZDT20
fatcat:tuccmwioarbg5forqiyr7bzm7i