Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning

Jasjeet Dhaliwal; Saurabh Shintre

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arXiv · 1806.10707

Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning

Jasjeet Dhaliwal·Saurabh Shintre

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Abstract

Deep neural networks are susceptible to small-but-specific adversarial perturbations capable of deceiving the network. This vulnerability can lead to potentially harmful consequences in security-critical applications. To address this vulnerability, we propose a novel metric called \emph{Gradient Similarity} that allows us to capture the influence of training data on test inputs. We show that \emph{Gradient Similarity} behaves differently for normal and adversarial inputs, and enables us to detect a variety of adversarial attacks with a near perfect ROC-AUC of 95-100\%. Even white-box adversaries equipped with perfect knowledge of the system cannot bypass our detector easily. On the MNIST dataset, white-box attacks are either detected with a high ROC-AUC of 87-96\%, or require very high distortion to bypass our detector.

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Jasjeet Dhaliwal, Saurabh Shintre. 2018-06-27. Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning. https://arxiv.org/abs/1806.10707

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