Hierarchical supervisions with two-stream network for Deepfake detection

Abstract

Recently, the quality of face generation and manipulation has reached impressive levels, making it difficult even for humans to distinguish real and fake faces. At the same time, methods to distinguish fake faces from reals came out, such as Deepfake detection. However, the task of Deepfake detection remains challenging, especially the low-quality fake images circulating on the Internet and the diversity of face generation methods. In this work, we propose a new Deepfake detection network that could effectively distinguish both high-quality and low-quality faces generated by various generation methods. First, we design a two-stream framework that incorporates a regular spatial stream and a frequency stream to handle the low-quality problem since we find that the frequency domain artifacts of low-quality images will be preserved. Second, we introduce hierarchical supervisions in a coarse-to-fine manner, which consists of a coarse binary classification branch to classify reals and fakes and a five-category classification branch to classify reals and four different types of fakes. Extensive experiments have proved the effectiveness of our framework on several widely used datasets.