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 link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exist shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.
Analysis of vulnerability of classifiers to universal perturbations and relation to the curvature of the decision boundary. The paper provides an interesting analysis linking the geometry of classifier decision boundaries to small universal adversarial perturbations. This paper discusses universal perturbations - perturbations that can mislead a trained classifier if added to most of input data points. The paper develops models which attempt to explain the existence of universal perturbations which fool neural networks