RoBIC: A benchmark suite for assessing classifiers robustness
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Abstract (arXiv)
Many defenses have emerged with the development of adversarial attacks. Models must be objectively evaluated accordingly. This paper systematically tackles this concern by proposing a new parameter-free benchmark we coin RoBIC. RoBIC fairly evaluates the robustness of image classifiers using a new half-distortion measure. It gauges the robustness of the network against white and black box attacks, independently of its accuracy. RoBIC is faster than the other available benchmarks. We present the significant differences in the robustness of 16 recent models as assessed by RoBIC.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 10 Feb 2021.
Builds on
24 of 35 references.
References · 24
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12 more
- 2020RobustBench: a standardized adversarial robustness benchmarkcited by 659
- 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
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