Attribution-driven Causal Analysis for Detection of Adversarial Examples

Understand

Attribution methods have been developed to explain the decision of a machine learning model on a given input.

• We use the Integrated Gradient method for finding attributions to define the causal neighborhood of an input by incrementally masking high attribution features.
• We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.
• Our study indicates that benign inputs are robust to the masking of high attribution features but adversarial inputs generated by the state-of-the-art adversarial attack methods such as DeepFool, FGSM, CW and PGD, are not robust to such masking.