  Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly
complex task that used to be unimaginable for machines to carry out. In doing
so, they do a lot of decision making which, depending on the application, may
be disastrous if gone wrong. This necessitates a formal argument that the
underlying neural networks satisfy certain desirable properties. Robustness is
one such key property for DNNs, particularly if they are being deployed in
safety or business critical applications. Informally speaking, a DNN is not
robust if very small changes to its input may affect the output in a
considerable way (e.g. changes the classification for that input). The task of
finding an adversarial example is to demonstrate this lack of robustness,
whenever applicable. While this is doable with the help of constrained
optimization techniques, scalability becomes a challenge due to large-sized
networks. This paper proposes the use of information gathered by preprocessing
the DNN to heavily simplify the optimization problem. Our experiments
substantiate that this is effective, and does significantly better than the
state-of-the-art.
