  Generative adversarial networks (GAN) are a class of powerful machine
learning techniques, where both a generative and discriminative model are
trained simultaneously. GANs have been used, for example, to successfully
generate "deep fake" images. A recent trend in malware research consists of
treating executables as images and employing image-based analysis techniques.
In this research, we generate fake malware images using auxiliary classifier
GANs (AC-GAN), and we consider the effectiveness of various techniques for
classifying the resulting images. Our results indicate that the resulting
multiclass classification problem is challenging, yet we can obtain strong
results when restricting the problem to distinguishing between real and fake
samples. While the AC-GAN generated images often appear to be very similar to
real malware images, we conclude that from a deep learning perspective, the
AC-GAN generated samples do not rise to the level of deep fake malware images.
