Certifiably Robust Interpretation in Deep Learning

TL;DR

This paper introduces a certifiable defense for deep learning interpretation methods, specifically enhancing the robustness of SmoothGrad against adversarial attacks, validated through experiments on ImageNet.

Contribution

It proposes a sparsified SmoothGrad method with certifiable robustness against adversarial perturbations in interpretation.

Findings

Sparsified SmoothGrad is certifiably robust against adversarial attacks.

Theoretical bounds for robustness are extended to interpretation methods.

Experimental validation on ImageNet confirms the effectiveness of the approach.

Abstract

Deep learning interpretation is essential to explain the reasoning behind model predictions. Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks. Although gradient-based saliency maps are popular methods for deep learning interpretation, recent works show that they can be vulnerable to adversarial attacks. In this paper, we address this problem and provide a certifiable defense method for deep learning interpretation. We show that a sparsified version of the popular SmoothGrad method, which computes the average saliency maps over random perturbations of the input, is certifiably robust against adversarial perturbations. We obtain this result by extending recent bounds for certifiably robust smooth classifiers to the interpretation setting. Experiments on ImageNet samples validate our theory.