Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks. However, it is currently very difficult to train a neural network that is both accurate and certifiably robust. In this work we take a step towards addressing this challenge. We prove that for every continuous function f, there exists a network n such that: (i) n approximates f arbitrarily close, and (ii) simple interval bound propagation of a region B through n yields a result that is arbitrarily close to the optimal output of f on B. Our result can be seen as a Universal Approximation Theorem for interval-certified ReLU networks. To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.
Universal Approximation with Certified Networks
Maximilian Baader, Matthew Mirman, Martin Vechev
ICLR 2020
@inproceedings{baader2020universal, title={Universal Approximation with Certified Networks}, author={Maximilian Baader, Matthew Mirman, Martin Vechev}, journal={ICLR}, year={2020}}