Spatial‐Frequency Feature Fusion Based Deepfake Detection With Mask Supervision

ABSTRACT In recent years, artificial intelligence technologies have been widely used, bringing security, convenience and certain risks. Deepfake techniques raise significant security concerns by manipulating facial images to create convincing but false representations. We introduce a novel mask‐supervision‐based deepfake detection method to improve detection performance in this context. Our approach corrects the model's focus on irrelevant regions through mask supervision, using pixel‐level labels to guide the model towards synthetic facial regions and ensure more accurate extraction of spatial features. In addition, we incorporate a frequency‐domain feature extraction module that exploits the robustness of frequency‐domain cues to compression artefacts. We first preprocess the input image and then feed it into the mask supervision and frequency‐domain feature extraction modules. The mask supervision module extracts intermediate features using the High‐Resolution Network (HRNet) and refines spatial features by guiding the prediction mask with the ground‐truth mask. The frequency‐domain module extracts features via the Discrete Cosine Transform (DCT) and filtering across different frequency bands. Finally, spatial and frequency‐domain features are concatenated and fed into a classification network to output the final prediction. Experimental results show that our method maintains good robustness in compressed scenarios.