MDCF-Net: Multi-Scale Dual-Branch Network for Compressed Face Forgery Detection

Face forgery detection aims to identify manipulated or altered facial images or videos created using artificial intelligence. Existing detection methods exhibit favorable performance on high-quality videos, but the videos in daily applications are commonly compressed into low-quality formats via social media. The detection difficulty is increased by the poor quality, indistinct detail features, and noises such as artifacts in these images or videos. To address this challenge, we propose a multi-scale dual-branch network for compressed face forgery, called MDCF-Net, effectively capturing cross-domain forgery features at various scales in compressed facial images. The MDCF-Net comprises two branches: an RGB domain branch utilizing Transformers to extract multi-scale fine-texture features from the original RGB images; a frequency domain branch designed to capture artifacts in low-quality videos by extracting global spectral features as a supplementary measure. Then, we introduce a feature fusion module (FFM) based on multi-head attention to merge diverse feature representations in a spatial-frequency complementary manner. Extensive comparative experiments on public datasets such as FaceForensics++, Celeb-DF, and WildDeepfake demonstrate the significant advantage of MDCF-Net in detecting highly compressed and low-quality forged images or videos, especially in achieving state-of-the-art performance on the FaceForensics++ low-quality dataset. Our approach presents a new perspective and technology for low-quality face forgery detection.