  It has been observed that deep learning architectures tend to make erroneous
decisions with high reliability for particularly designed adversarial
instances. In this work, we show that the perturbation analysis of these
architectures provides a framework for generating adversarial instances by
convex programming which, for classification tasks, is able to recover variants
of existing non-adaptive adversarial methods. The proposed framework can be
used for the design of adversarial noise under various desirable constraints
and different types of networks. Moreover, this framework is capable of
explaining various existing adversarial methods and can be used to derive new
algorithms as well. We make use of these results to obtain novel algorithms.
The experiments show the competitive performance of the obtained solutions, in
terms of fooling ratio, when benchmarked with well-known adversarial methods.
