RAB: Provable Robustness Against Backdoor Attacks

TL;DR

This paper introduces RAB, a novel training method that certifies and enhances the robustness of various machine learning models, including deep neural networks and K-NN classifiers, against backdoor and evasion attacks using randomized smoothing techniques.

Contribution

It presents the first robust training process, RAB, with proven tight robustness bounds against backdoor attacks and an exact smooth-training algorithm for simple models.

Findings

RAB effectively certifies robustness against backdoor attacks.

The exact smooth-training algorithm improves efficiency for simple models.

Empirical results demonstrate enhanced robustness across multiple datasets and models.

Abstract

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robustness against evasion attacks; however, the provable robustness against backdoor attacks still remains largely unexplored. In this paper, we focus on certifying the machine learning model robustness against general threat models, especially backdoor attacks. We first provide a unified framework via randomized smoothing techniques and show how it can be instantiated to certify the robustness against both evasion and backdoor attacks. We then propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. We prove the robustness bound for machine learning models trained with RAB and prove that our robustness bound is tight. In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models. Empirically, we conduct comprehensive experiments for different machine learning (ML) models such as DNNs, support vector machines, and K-NN models on MNIST, CIFAR-10, and ImageNette datasets and provide the first benchmark for certified robustness against backdoor attacks. In addition, we evaluate K-NN models on a spambase tabular dataset to demonstrate the advantages of the proposed exact algorithm. Both the theoretic analysis and the comprehensive evaluation on diverse ML models and datasets shed light on further robust learning strategies against general training time attacks.