With Friends Like These, Who Needs Adversaries?
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The vulnerability of deep image classification networks to adversarial attack is now well known, but less well understood. Via a novel experimental analysis, we illustrate some facts about deep convolutional networks (DCNs) that shed new light on their behaviour and its connection to the problem of adversaries, with two key results. The first is a straightforward explanation of the existence of universal adversarial perturbations and their association with specific class identities, obtained by analysing the properties of nets' logit responses as functions of 1D movements along specific image-space directions. The second is the clear demonstration of the tight coupling between classification performance and vulnerability to adversarial attack within the spaces spanned by these directions. Prior work has noted the importance of low-dimensional subspaces in adversarial vulnerability: we illustrate that this likewise represents the nets' notion of saliency. In all, we provide a digestible perspective from which to understand previously reported results which have appeared disjoint or contradictory, with implications for efforts to construct neural nets that are both accurate and robust to adversarial attack.
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