Gene

Deep neural networks

Smoothing

Robustness (evolution)

Scalability

Biochemistry

Computer science

Database

Artificial intelligence

Chemistry

Artificial neural network

Margin (machine learning)

Machine learning

Computer vision

Adversarial system

arXiv (Cornell University)

Recent works have shown the effectiveness of randomized smoothing as a scalable technique for building neural network-based classifiers that are provably robust to $\ell_2$-norm adversarial perturbations. In this paper, we employ adversarial training to improve the performance of randomized smoothing. We design an adapted attack for smoothed classifiers, and we show how this attack can be used in an adversarial training setting to boost the provable robustness of smoothed classifiers. We demonstrate through extensive experimentation that our method consistently outperforms all existing provably $\ell_2$-robust classifiers by a significant margin on ImageNet and CIFAR-10, establishing the state-of-the-art for provable $\ell_2$-defenses. Moreover, we find that pre-training and semi-supervised learning boost adversarially trained smoothed classifiers even further. Our code and trained models are available at http://github.com/Hadisalman/smoothing-adversarial.

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers