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

Understand

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\%.