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Deep Neural Networks (DNNs) are everywhere, frequently performing a fairlycomplex task that used to be unimaginable for machines to carry out. In doingso, they do a lot of decision making which, depending on the application, maybe disastrous if gone wrong. This necessitates a formal argument that theunderlying neural networks satisfy certain desirable properties. Robustness isone such key property for DNNs, particularly if they are being deployed insafety or business critical applications. Informally speaking, a DNN is notrobust if very small changes to its input may affect the output in aconsiderable way (e.g. changes the classification for that input). The task offinding an adversarial example is to demonstrate this lack of robustness,whenever applicable. While this is doable with the help of constrainedoptimization techniques, scalability becomes a challenge due to large-sizednetworks. This paper proposes the use of information gathered by preprocessingthe DNN to heavily simplify the optimization problem. Our experimentssubstantiate that this is effective, and does significantly better than thestate-of-the-art.

Efficiently Finding Adversarial Examples with DNN Preprocessing