Certifiably Robust Interpretation in Deep Learning
By Alexander Levine, Sahil Singla, Soheil Feizi. 2019
65 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- J. Kolter
- Elan Rosenfeld
- Jeremy M. Cohen
- Matthias Hein
- Cho-Jui Hsieh
- Huan Zhang
- Maksym Andriushchenko
- Pieter Abbeel
Citations in the corpus
- Certified Adversarial Robustness via Randomized Smoothing (2019, background): “(2)After the dissemination of this work, a more general result was published in Levine et al. (2019); Salman et al.”