Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation. However, these certification based methods treat all the examples equally regardless of their vulnerability and true adversarial distribution, limiting the model's potential in achieving optimal verifiable accuracy. In the paper, we propose new methods to include the customized weight distribution and automatic schedule tuning methods on the perturbation schedule. These methods are generally applicable to all the verification-based robust training with almost no additional computational cost. Our results show improvement on MNIST with $\epsilon = 0.3$ and CIFAR on $\epsilon = 8/255$ for both IBP and CROWN-IBP based methods.
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