Deep neural networks (DNNs) achieved a significant performance leap over the past decade and have been widely adopted in various industrial domains. However, a fundamental problem regarding DNN robustness is still not adequately addressed, which can potentially lead to many quality issues after deployment, e.g., safety, security, and reliability. An adversarial attack is one of the most commonly investigated techniques to penetrate a DNN by misleading the DNN’s decision by generating minor perturbation on original inputs. More importantly, the adversarial attack is a crucial way to assess, estimate, and understand the robustness boundary of a DNN. Intuitively, a stronger adversarial attack can help obtain a tighter robustness boundary to understand the potentially worst case when a DNN is deployed. To push this further, in this paper, we propose DeepRover, a fuzzing-based blackbox attack for deep neural networks used for image classification. We show that DeepRover is more effective and query-efficient in generating adversarial examples than state-of-the-art blackbox attacks. Moreover, DeepRover can find adversarial examples at a finer-grained level than other approaches.