  Recent works have developed several methods of defending neural networks
against adversarial attacks with certified guarantees. However, these
techniques can be computationally costly due to the use of certification during
training. We develop a new regularizer that is both more efficient than
existing certified defenses, requiring only one additional forward propagation
through a network, and can be used to train networks with similar certified
accuracy. Through experiments on MNIST and CIFAR-10 we demonstrate improvements
in training speed and comparable certified accuracy compared to
state-of-the-art certified defenses.
