Abstract Interpretability of a deep neural network aims to explain the rationale behind its decisions and enable the users to understand the intelligent agents, which has become an important issue due to its importance in practical applications. To address this issue, we develop a Distillation Guided Routing method, which is a flexible framework to interpret a deep neural network by identifying critical data routing paths and analyzing the functional processing behavior of the corresponding layers. Specifically, we propose to discover the critical nodes on the data routing paths during network inferring prediction for individual input samples by learning associated control gates for each layer’s output channel. The routing paths can, therefore, be represented based on the responses of concatenated control gates from all the layers, which reflect the network’s semantic selectivity regarding to the input patterns and more detailed functional process across different layer levels. Based on the discoveries, we propose an adversarial sample detection algorithm by learning a classifier to discriminate whether the critical data routing paths are from real or adversarial samples. Experiments demonstrate that our algorithm can effectively achieve high defense rate with minor training overhead.