Transferable Adversarial Robustness using Adversarially Trained Autoencoders
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Machine learning has proven to be an extremely useful tool for solving complex problems in many application domains. This prevalence makes it an attractive target for malicious actors. Adversarial machine learning is a well-studied field of research in which an adversary seeks to cause predicable errors in a machine learning algorithm through careful manipulation of the input. In response, numerous techniques have been proposed to harden machine learning algorithms and mitigate the effect of adversarial attacks. Of these techniques, adversarial training, which augments the training data with adversarial inputs, has proven to be an effective defensive technique. However, adversarial training is computationally expensive and the improvements in adversarial performance are limited to a single model. In this paper, we propose Adversarially-Trained Autoencoder Augmentation, the first transferable adversarial defense that is robust to certain adaptive adversaries. We disentangle adversarial robustness from the classification pipeline by adversarially training an autoencoder with respect to the classification loss. We show that our approach achieves comparable results to state-of-the-art adversarially trained models on the MNIST, Fashion-MNIST, and CIFAR-10 datasets. Furthermore, we can transfer our approach to other vulnerable models and improve their adversarial performance without additional training. Finally, we combine our defense with ensemble methods and parallelize adversarial training across multiple vulnerable pre-trained models. In a single adversarial training session, the autoencoder can achieve adversarial performance on the vulnerable models that is comparable or better than standard adversarial training.
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