  As the name suggests, image spam is spam email that has been embedded in an
image. Image spam was developed in an effort to evade text-based filters.
Modern deep learning-based classifiers perform well in detecting typical image
spam that is seen in the wild. In this chapter, we evaluate numerous
adversarial techniques for the purpose of attacking deep learning-based image
spam classifiers. Of the techniques tested, we find that universal perturbation
performs best. Using universal adversarial perturbations, we propose and
analyze a new transformation-based adversarial attack that enables us to create
tailored "natural perturbations" in image spam. The resulting spam images
benefit from both the presence of concentrated natural features and a universal
adversarial perturbation. We show that the proposed technique outperforms
existing adversarial attacks in terms of accuracy reduction, computation time
per example, and perturbation distance. We apply our technique to create a
dataset of adversarial spam images, which can serve as a challenge dataset for
future research in image spam detection.
