Current vehicle detection method for fake plate vehicles has a high computational complexity, low detection accuracy, lack of robustness. This paper presents of fake plate vehicles detection method based on multitask Faster RCNN (regionbased convolutional neural network). Firstly, spatiotemporal constraint is used to obtain the suspected fake plate vehicle. Then, front part of the vehicle is located in the image using Faster RCNN. Next, the public face (basic characteristics of a vehicle) of suspicious fake plate vehicles is contrasted. In further, the subtle features of a private face (Annual inspection certificate for vehicles) is contrasted. This hierarchical visual inspection method, detected from macroscopic features of vehicles to microscopic features, has the advantages of fast detection speed, high robustness, strong generalization ability, convenient deployment and high detection precision. Experimental results show that detection accuracy are 99.39% and 99.22% on the Vehicle ID data set and the Hangzhou bayonet data set, respectively.