With Friends Like These, Who Needs Adversaries?

Abstract

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 for image classification that shed new light on their behaviour and how it connects to the problem of adversaries. In short, the celebrated performance of these networks and their vulnerability to adversarial attack are simply two sides of the same coin: the input image-space directions along which the networks are most vulnerable to attack are the same directions which they use to achieve their classification performance in the first place. We develop this result in two main steps. The first uncovers the fact that classes tend to be associated with specific image-space directions. This is shown by an examination of the class-score outputs of nets as functions of 1D movements along these directions. This provides a novel perspective on the existence of universal adversarial perturbations. The second is a clear demonstration of the tight coupling between classification performance and vulnerability to adversarial attack within the spaces spanned by these directions. Thus, our analysis resolves the apparent contradiction between accuracy and vulnerability. It provides a new perspective on much of the prior art and reveals profound implications for efforts to construct neural nets that are both accurate and robust to adversarial attack.

arXiv commentPublished in this form at NeurIPS 2018

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• Unadversarial Examples: Designing Objects for Robust Vision
• On the effectiveness of adversarial training against common corruptions
• Hold me tight! Influence of discriminative features on deep network boundaries
• Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
• Investigating Decision Boundaries of Trained Neural Networks
• Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
• Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis