Aiming at the problems of excessive feature noise a large amount of data and low detection accuracy in the detection of fake videos an algorithm was proposed to improve the Gaussian filter to denoise the landmark points and improve the deep network model so as to improve the accuracy. The Gaussian filter is extended to the spatial domain and the value domain and the high-frequency noise is retained as much as possible while filtering out the low-frequency noise so that the accuracy of the marker points can be improved in the subsequent processing of the feature data. The characteristic data is used instead of the original data to reduce the amount of data which greatly reduces the amount of data sent to the network effectively shortens the training time and reduces the number of network parameters and can quickly identify fake videos in practical applications. In addition according to the difference of face attribute feature points dual-flow neural networks with different depths are used to learn how to identify fake videos more effectively. The experimental results show that the improved Gaussian Filter network algorithm IGFNet effectively increases the accuracy of real and fake face detection. Compared with the current excellent methods such as Meso4 Xecption and LRNet the accuracy of IGFNet has been improved to different degrees especially in the test across datasets. In the test of compressed fake videos IGFNet has the lowest degree of generalization degradation showing strong robustness. The ability of IGFNet to discriminate deep forgery images is intuitively judged by improving the network and adding the gradient heat map.