Deepfake Detection Based on the Adaptive Fusion of Spatial-Frequency Features

Detecting deepfake media remains an ongoing challenge, particularly as forgery techniques rapidly evolve and become increasingly diverse. Existing face forgery detection models typically attempt to discriminate fake images by identifying either spatial artifacts (e.g., generative distortions and blending inconsistencies) or predominantly frequency-based artifacts (e.g., GAN fingerprints). However, a singular focus on a single type of forgery cue can lead to limited model performance. In this work, we propose a novel cross-domain approach that leverages a combination of both spatial and frequency-aware cues to enhance deepfake detection. First, we extract wavelet features using wavelet transformation and residual features using a specialized frequency domain filter. These complementary feature representations are then concatenated to obtain a composite frequency domain feature set. Furthermore, we introduce an adaptive feature fusion module that integrates the RGB color features of the image with the composite frequency domain features, resulting in a rich, multifaceted set of classification features. Extensive experiments conducted on benchmark deepfake detection datasets demonstrate the effectiveness of our method. Notably, the accuracy of our method on the challenging FF++ dataset is mostly above 98%, showcasing its strong performance in reliably identifying deepfake images across diverse forgery techniques.