Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

international conference on learning representations, 2018.

Cited by: 81|Bibtex|Views23
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Abstract:

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal lin...More

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