Efficient and Effective Augmentation Strategy for Adversarial Training

The sample complexity of Adversarial training is known to be significantly higher than standard ERM based training. Although complex augmentation techniques have led to large gains in standard training, they have not been successful with Adversarial Training. In this work, we propose Diverse Augmentation based Joint Adversarial Training (DAJAT) that uses a combination of simple and complex augmentations with separate batch normalization layers to handle the conflicting goals of enhancing the diversity of the training dataset, while being close to the test distribution. We further introduce a Jensen-Shannon divergence loss to encourage the joint learning of the diverse augmentations, thereby allowing simple augmentations to guide the learning of complex ones. Lastly, to improve the computational efficiency of the proposed method, we propose and utilize a two-step defense, Ascending Constraint Adversarial Training (ACAT) that uses an increasing epsilon schedule and weight-space smoothing to prevent gradient masking. The proposed method achieves better performance compared to existing methods on the RobustBench Leaderboard for CIFAR-10 and CIFAR-100 on ResNet-18 and WideResNet-34-10 architectures.