DeepAI

Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation

Recently, techniques have been developed to provably guarantee the robustness of a classifier to adversarial perturbations of bounded L_1 and L_2 magnitudes by using randomized smoothing: the robust classification is a consensus of base classifications on randomly noised samples where the noise is additive. In this paper, we extend this technique to the L_0 threat model. We propose an efficient and certifiably robust defense against sparse adversarial attacks by randomly ablating input features, rather than using additive noise. Experimentally, on MNIST, we can certify the classifications of over 50 images to be robust to any distortion of at most 8 pixels. This is comparable to the observed empirical robustness of unprotected classifiers on MNIST to modern L_0 attacks, demonstrating the tightness of the proposed robustness certificate. We also evaluate our certificate on ImageNet and CIFAR-10. Our certificates represent an improvement on those provided in a concurrent work (Lee et al. 2019) which uses random noise rather than ablation (median certificates of 8 pixels versus 4 pixels on MNIST; 16 pixels versus 1 pixel on ImageNet.) Additionally, we empirically demonstrate that our classifier is highly robust to modern sparse adversarial attacks on MNIST. Our classifications are robust, in median, to adversarial perturbations of up to 31 pixels, compared to 22 pixels reported as the state-of-the-art defense, at the cost of a slight decrease (around 2.3 available at https://github.com/alevine0/randomizedAblation/.

• 16 publications
• 64 publications
02/25/2020

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks

Patch adversarial attacks on images, in which the attacker can distort p...
02/08/2019

Certified Adversarial Robustness via Randomized Smoothing

Recent work has shown that any classifier which classifies well under Ga...
05/08/2021

Recently, few certified defense methods have been developed to provably ...
03/25/2019

Defending against Whitebox Adversarial Attacks via Randomized Discretization

Adversarial perturbations dramatically decrease the accuracy of state-of...
12/10/2021

Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks

Deep neural networks have become the driving force of modern image recog...
12/11/2019

What it Thinks is Important is Important: Robustness Transfers through Input Gradients

Adversarial perturbations are imperceptible changes to input pixels that...
02/14/2020

Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images

We show a hardness result for random smoothing to achieve certified adve...