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• W3128984943 abstract "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." @default.
• W3128984943 created "2021-02-15" @default.
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• W3128984943 date "2021-02-10" @default.
• W3128984943 modified "2023-09-28" @default.
• W3128984943 title "RoBIC: A benchmark suite for assessing classifiers robustness." @default.
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