Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach

Abstract

There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output a prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation.