  Although deep neural networks have been very successful in
image-classification tasks, they are prone to adversarial attacks. To generate
adversarial inputs, there has emerged a wide variety of techniques, such as
black- and whitebox attacks for neural networks. In this paper, we present
DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image
classifiers. Despite its simplicity, DeepSearch is shown to be more effective
in finding adversarial inputs than state-of-the-art blackbox approaches.
DeepSearch is additionally able to generate the most subtle adversarial inputs
in comparison to these approaches.
