The DeepFake video detection method based on convolutional neural networks has a poor performance in the dataset of hard compressed DeepFake video. And a large number of false tests will occur to the real data. To solve this problem, a networks model detection method for super-resolution reconstruction of DeepFake video is proposed. First of all, the face area of real data is processed by Gaussian blur, which is converted into negative data, and the real data and processing data are input into neural network for training. Then the residual network is used for super-resolution reconstruction of test data. Finally, the trained model is used to test the video after super-resolution reconstruction. Experiments show that the proposed method can reduce the false detection rate and improve the accuracy in detection of single frames.