GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples

TL;DR

GraN is a fast, parameter-efficient method for detecting adversarial and misclassified examples in DNNs, based on layer-wise gradient norms, achieving state-of-the-art results across various scenarios.

Contribution

Introduces GraN, a novel gradient-norm based detection method that is both computationally efficient and easily adaptable to any deep neural network.

Findings

Achieves state-of-the-art detection performance.

Requires less runtime and fewer parameters than existing methods.

Effective across multiple problem setups.

Abstract

Deep neural networks (DNNs) are vulnerable to adversarial examples and other data perturbations. Especially in safety critical applications of DNNs, it is therefore crucial to detect misclassified samples. The current state-of-the-art detection methods require either significantly more runtime or more parameters than the original network itself. This paper therefore proposes GraN, a time- and parameter-efficient method that is easily adaptable to any DNN. GraN is based on the layer-wise norm of the DNN's gradient regarding the loss of the current input-output combination, which can be computed via backpropagation. GraN achieves state-of-the-art performance on numerous problem set-ups.