Deep Neural Network (DNN) is proved to be vulnerable to adversarial sample attacks in many key deep learning systems such as face recognition and intelligent driving. And the detection of various types of adversarial samples has problems of insufficient detection and low detection efficiency. Therefore, a deep learning model oriented adversarial sample difference detection method was proposed. Firstly, the residual neural network model commonly used in industrial production was constructed as the model of the adversarial sample generation and detection system. Then, multiple kinds of adversarial attacks were used to attack the deep learning model to generate adversarial sample groups. Finally, a sample difference detection system was constructed, containing total 7 adversarial sample difference detection methods in sample confidence detection, perception detection and anti-interference degree detection. Empirical research was carried out by the constructed method on the MNIST and Cifar-10 datasets. The results show that the adversarial samples belonging to different adversarial attacks have obvious differences in the performance detection on confidence, perception and anti-interference degrees, for example, in the detection of confidence and anti-interference, the adversarial samples with excellent performance indicators in perception show significant insufficiencies compared to other types of adversarial samples. At the same time, it is proved that there is consistency of the differences in the two datasets. By using this detection method, the comprehensiveness and diversity of the model's detection of adversarial samples can be effectively improved.