  We address the problem of verifying neural-based perception systems
implemented by convolutional neural networks. We define a notion of local
robustness based on affine and photometric transformations. We show the notion
cannot be captured by previously employed notions of robustness. The method
proposed is based on reachability analysis for feed-forward neural networks and
relies on MILP encodings of both the CNNs and transformations under question.
We present an implementation and discuss the experimental results obtained for
a CNN trained from the MNIST data set.
