Blind detection of glow-based facial forgery

Abstract

With the rapid development of artificial intelligence technologies, various generative models can synthesize fake face images with photo-realistic effects. Glow, a generative flow using invertible 1×1 convolution, is a state-of-the-art technique for efficient synthesis of face images with high resolution and fidelity. However, facial forgeries bring serious challenges to morality, ethics and public confidence. Especially, facial forgeries might change the semantic content conveyed by a face image. A Convolutional Neural Network (CNN) based model, namely SCnet, is proposed to expose the Glow-based facial forgery. Specifically, an image sharpening operator is embedded in the convolutional layer as the pre-processing layer of the network to highlight the traces left by Glow. Then, SCnet is specifically designed to automatically learn high-level forensics features from pre-processing results. Moreover, a fake face dataset is built by exploiting the CelebA face image dataset and the Glow-based forgery technique. A series of experiments are conducted to prove the effectiveness of the proposed approach. Experimental results show that the proposed approach achieves a classification accuracy up to 95.92% under various post-processing operations.