DeepSearch: Simple and Effective Blackbox Fuzzing of Deep Neural Networks

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