Adversarial Training for Free!

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train.Comment: Accepted to NeurIPS 201

Certifiable Black-Box Attack: Ensuring Provably Successful Attack for Adversarial Examples

Black-box adversarial attacks have shown strong potential to subvert machine learning models. Existing black-box adversarial attacks craft the adversarial examples by iteratively querying the target model and/or leveraging the transferability of a local surrogate model. Whether such attack can succeed remains unknown to the adversary when empirically designing the attack. In this paper, to our best knowledge, we take the first step to study a new paradigm of adversarial attacks -- certifiable black-box attack that can guarantee the attack success rate of the crafted adversarial examples. Specifically, we revise the randomized smoothing to establish novel theories for ensuring the attack success rate of the adversarial examples. To craft the adversarial examples with the certifiable attack success rate (CASR) guarantee, we design several novel techniques, including a randomized query method to query the target model, an initialization method with smoothed self-supervised perturbation to derive certifiable adversarial examples, and a geometric shifting method to reduce the perturbation size of the certifiable adversarial examples for better imperceptibility. We have comprehensively evaluated the performance of the certifiable black-box attack on CIFAR10 and ImageNet datasets against different levels of defenses. Both theoretical and experimental results have validated the effectiveness of the proposed certifiable attack