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RoBIC: A benchmark suite for assessing classifiers robustness

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 نشر من قبل Thibault Maho
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
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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.



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