Multiple Feature Mining Based on Local Correlation and Frequency Information for Face Forgery Detection

Shuai Liu, Qian Jiang, Xin Jin, Zhenli He, Wei Zhou, Shaowen Yao, Qiannian Wang

Abstract: As facial image manipulation techniques developed, deep fake detection attracted extensive attentions. Although researchers have made remarkable progresses in deepfake detection recently, which is still suffering from two limitations: a) current detectors achieve high accuracy in the high-quality videos and images, but it is hard to capture local and subtle artifacts in the low-quality and high-compression media; b) few of deep fake detection methods gain satisfying performance under cross-database scenario, because detector overfit to specific color textures producing by same manipulation algorithm. Inspired the above issues, this paper proposes a novel framework fusing local related features and frequency information to mine the forgery patterns. Firstly, we design multi-feature enhancement module, which amplifies implicit local disc repancies and capture spatial correlation from three shallow feature layers and high-level semantic layer guided by attention maps. Secondly, dual frequency decomposition module is proposed for disassembling high-frequency and low-frequency features, the forgery artifacts are exposed after dual cross attention block processing in the frequency spectrum. Features from the two streams are fused to the classification for the final result. Comprehensive experiments demonstrate the superior performance of our proposed approach in the low-quality benchmark database and cross-dataset sce-nario.