Certifiably Robust Interpretation in Deep Learning
This addresses the need for reliable interpretation methods in sensitive domains like medical applications, though it is incremental as it extends existing robustness bounds to interpretation.
The paper tackles the vulnerability of gradient-based saliency maps in deep learning interpretation to adversarial attacks by proposing a certifiable defense method, showing that a sparsified SmoothGrad version is robust against perturbations, with experiments validating the theory on ImageNet samples.
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.