  Deep neural networks have been shown to be vulnerable to adversarial attacks
that perturb inputs based on semantic features. Existing robustness analyzers
can reason about semantic feature neighborhoods to increase the networks'
reliability. However, despite the significant progress in these techniques,
they still struggle to scale to deep networks and large neighborhoods. In this
work, we introduce VeeP, an active learning approach that splits the
verification process into a series of smaller verification steps, each is
submitted to an existing robustness analyzer. The key idea is to build on prior
steps to predict the next optimal step. The optimal step is predicted by
estimating the certification velocity and sensitivity via parametric
regression. We evaluate VeeP on MNIST, Fashion-MNIST, CIFAR-10 and ImageNet and
show that it can analyze neighborhoods of various features: brightness,
contrast, hue, saturation, and lightness. We show that, on average, given a 90
minute timeout, VeeP verifies 96% of the maximally certifiable neighborhoods
within 29 minutes, while existing splitting approaches verify, on average, 73%
of the maximally certifiable neighborhoods within 58 minutes.
