Fuzzing is considered to be an essential approach to guarantee the reliability of deep neural networks (DNNs) based systems. The DNN fuzzing leverages various inputs prioritization methods to guide the testing process. The current research mainly focus on constructing testing metrics that symbolize the logical representation of the DNN to guide the generation of test cases, which neglects the potential performance brought by implementing heuristic algorithm. Moreover, the straightforward implementation of queue structure can not represent the metamorphic relationships between generated inputs in DNN fuzzing. Therefore, developing the appropriate heuristic algorithm-based inputs prioritization method is critical to improve the performance of DNN fuzzers. In this paper, we propose a Monte Carlo Tree Search (MCTS) based inputs prioritization method called $E{x}^2$ (Exploration and Exploitation) that formulates DNN testing exploration as the sequential decision process. The technique introduces an innovative tree-structure design that schedules inputs from the statistical perspective. Different from traditional DNN testing, the batch pool is maintained in the form of nodes in MCTS. The links between nodes precisely represent the metamorphic relationship between input batches, which indicates the potential value for in-depth search. Furthermore, a novel simulation mechanism is implemented to adapt MCTS in DNN testing, which attain better coverage feedback. The effectiveness of our method is comprehensively investigated on six popular deep learning models from LeNet and VGG families. The comparison experiments are conducted between DeepHunter, TensorFuzz, and DeepSmartFuzzer to demonstrate efficacy on various testing metrics. The experimental results show that the $E{x}^2$ significantly enhance the coverage gain of DNN fuzzing up to 30% against the best performance in comparison groups.

中文翻译：

---

Ex2：用于模糊深度神经网络的基于蒙特卡洛树搜索的测试输入优先级排序

模糊测试被认为是保证基于深度神经网络（DNN）的系统可靠性的基本方法。DNN 模糊测试利用各种输入优先级排序方法来指导测试过程。目前的研究主要集中在构建象征DNN逻辑表示的测试指标来指导测试用例的生成，而忽略了实现启发式算法所带来的潜在性能。此外，队列结构的直接实现不能表示 DNN 模糊测试中生成的输入之间的变形关系。因此，开发适当的基于启发式算法的输入优先级排序方法对于提高 DNN 模糊器的性能至关重要。在本文中，$乙X^{\mathrm{2个}}$(Exploration and Exploitation) 将 DNN 测试探索制定为顺序决策过程。该技术引入了一种创新的树结构设计，可以从统计角度安排输入。不同于传统的DNN测试，MCTS中batch pool以节点的形式维护。节点之间的链接精确地代表了输入批次之间的变质关系，这表明了深度搜索的潜在价值。此外，实施了一种新颖的模拟机制以在 DNN 测试中采用 MCTS，从而获得更好的覆盖反馈。我们的方法的有效性在来自 LeNet 和 VGG 系列的六种流行深度学习模型上进行了全面研究。对比实验在 DeepHunter、TensorFuzz、和 DeepSmartFuzzer 来证明各种测试指标的有效性。实验结果表明$乙X^{\mathrm{2个}}$与比较组中的最佳性能相比，显着提高了 DNN 模糊测试的覆盖增益高达 30%。