  Deepfake detection refers to detecting artificially generated or edited faces
in images or videos, which plays an essential role in visual information
security. Despite promising progress in recent years, Deepfake detection
remains a challenging problem due to the complexity and variability of face
forgery techniques. Existing Deepfake detection methods are often devoted to
extracting features by designing sophisticated networks but ignore the
influence of perceptual quality of faces. Considering the complexity of the
quality distribution of both real and fake faces, we propose a novel Deepfake
detection framework named DeepFidelity to adaptively distinguish real and fake
faces with varying image quality by mining the perceptual forgery fidelity of
face images. Specifically, we improve the model's ability to identify complex
samples by mapping real and fake face data of different qualities to different
scores to distinguish them in a more detailed way. In addition, we propose a
network structure called Symmetric Spatial Attention Augmentation based vision
Transformer (SSAAFormer), which uses the symmetry of face images to promote the
network to model the geographic long-distance relationship at the shallow level
and augment local features. Extensive experiments on multiple benchmark
datasets demonstrate the superiority of the proposed method over
state-of-the-art methods.
