Research On Face-Swap Images And Its Detection Based On Deep Learning

The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning.The face-swap manipulations by DeepFake are so real that it is tricky to distinguish authenticity through automatic or manual detection.Face-swap technology can be used for both positive and illegal purposes.Therefore,the study of the most advanced face-swap technology is not only the need of positive technology development,but also the need of resisting and detecting face forgery.In this paper,we focus on the face-swap technology and forgery detection technology.The main works are as follows:In order to solve the problem of obvious forgery trace in complex pose or illumination in existing face-swap algorithms,a face-swap method based on 3D reconstruction is proposed.First,an end-to-end position map regression network is trained to realize the facial 3D reconstruction of a single image.Then,the target 3D image is generated by the 3D reconstruction network.According to the reconstructed 3D shape,the corresponding color space and vertices are obtained.Finally,combining the color space of the source image and the vertex information of the target image,the final face is rendered.Experiments show that the face-swap method based on 3D reconstruction is effective and robust.Especially,compared with the result of face-swap using 2D face image,the images generated by this method are more realistic and natural in various complex pose or illumination scene.In view of the complexity and long training period of most facial forgery detection algorithms,a lightweight deep learning model is proposed to detect face-swap forgery images.First,the generation principle of deepfake algorithm is analyzed.Because deepfake can only generate limited resolution,this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background,which would leave distinctive counterfeit traces.Then,the error level analysis method is used to highlight the compression ratio difference of the stitching image.Finally,a binary deep learning model is constructed to detect whether the image is a forgery or not.The experimental results show that the detection accuracy of the face forgery image based on the convolutional neural network architecture can reach more than 97%.Compared with the network models of vgg16,resnet50,resnet101 and resnet152,the model of this method is not only lightweight,easy to train,but also more efficient.Specifically,without loss of accuracy,the amount of computation can be significantly reduced.