Smooth-Reduce: Leveraging Patches for Improved Certified Robustness

Ameya Joshi, Minh Pham, Minsu Cho, Leonid Boytsov, Filipe Condessa, J Zico Kolter, Chinmay Hegde

Keywords: Adversarial defenses, Certifiable defenses, Randomized Smoothing, Ensemble Models, Robust Video Classifiers

TL;DR: We present a novel, patch based approach to simulate ensembles that improve randomized smoothing performance.

Abstract: Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require augmenting data with large amounts of noise, which leads to significant drops in accuracy. We propose a training-free, modified smoothing approach, Smooth-Reduce, that leverages patching and aggregation to provide improved classifier certificates. Our algorithm classifies overlapping patches extracted from an input image, and aggregates the predicted logits to certify a larger radius around the input. We study two aggregation schemes --- max and mean --- and show that both approaches provide better certificates in terms of certified accuracy, average certified radii and abstention rates as compared to concurrent approaches. We also provide theoretical guarantees for such certificates, and empirically show significant improvements over other randomized smoothing methods that require expensive retraining. Further, we extend our approach to videos and provide meaningful certificates for video classifiers.

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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/smooth-reduce-leveraging-patches-for-improved/code)