  There has been an increased interest in the application of convolutional
neural networks for image based malware classification, but the susceptibility
of neural networks to adversarial examples allows malicious actors to evade
classifiers. Adversarial examples are usually generated by adding small
perturbations to the input that are unrecognizable to humans, but the same
approach is not effective with malware. In general, these perturbations cause
changes in the byte sequences that change the initial functionality or result
in un-executable binaries. We present a generative model for executable
adversarial malware examples using obfuscation that achieves a high
misclassification rate, up to 100% and 98% in white-box and black-box settings
respectively, and demonstrates transferability. We further evaluate the
effectiveness of the proposed method by reporting insignificant change in the
evasion rate of our adversarial examples against popular defense strategies.
