SHIELD uses a hypernetwork with IBP training and a new Interval MixUp technique to achieve certified robustness in continual learning, reporting state-of-the-art adversarial accuracy on MNIST, CIFAR-100, and miniImageNet splits.
Training Ensembles to Detect Adversarial Examples
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abstract
We propose a new ensemble method for detecting and classifying adversarial examples generated by state-of-the-art attacks, including DeepFool and C&W. Our method works by training the members of an ensemble to have low classification error on random benign examples while simultaneously minimizing agreement on examples outside the training distribution. We evaluate on both MNIST and CIFAR-10, against oblivious and both white- and black-box adversaries.
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SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense
SHIELD uses a hypernetwork with IBP training and a new Interval MixUp technique to achieve certified robustness in continual learning, reporting state-of-the-art adversarial accuracy on MNIST, CIFAR-100, and miniImageNet splits.