Using Video Restoration to Improve Face Forgery Detection Based on Low-quality Video

The face forgery detection model based on XceptionNet has made great achievements in the detection field. However, it is still a challenge to detect fake faces in low-quality video images, because the low-quality video image has an insufficient resolution, which leads to the loss of details of the video image, thus resulting in image blurring. To solve this problem, this paper proposes a low-quality video face forgery detection method based on video recovery. This method mainly uses the Pyramid, Cascading, and Deformable convolution(PCD) module and the spatiotemporal attention (TSA) fusion module to restore low-quality video face images, and then obtains the restored feature map. And then the restored feature map is fed into the Xception classification network for face forgery detection. Moreover, The pre-training model parameters based on ImageNet makes the training model converge on 2GPU days. The results show that this method has a good experimental effect on the test data set.