LDFnet: Lightweight Dynamic Fusion Network for Face Forgery Detection by Integrating Local Artifacts and Global Texture Information

Face forgery detection has become a new research hotspot. Though existing detection works have achieved impressive performance, they are difficult to achieve a proper trade-off between detection accuracy and model complexity. To solve this problem, we design some low-complexity modules and construct a lightweight dynamic fusion network (LDFnet) to achieve high accuracy and lightweight face forgery detection. Firstly, we regard significant local visual artifacts as a correct semantic feature needed for detection. A spatial group-wise enhance (SGE) module is introduced as a supervision to suppress possible noise and capture local artifacts. Secondly, we design a manipulation trace extraction block (TraceBlock), which can replace vanilla convolution to achieve global inference, thus capturing the texture information in the global scope. Based on TraceBlock, we construct a global texture representation (GTR) network to extract global manipulation features hierarchically. Finally, we design a dynamic fusion mechanism (DFM) to fully fuse local and global clues, and dynamically generate a more discriminating feature representation. Extensive experimental results show that the proposed LDFnet is significantly superior to the previous detection works on some popular face forgery datasets, such as FF++, DFDC, CelebDF and HFF. In particular, LDFnet only uses 963k model parameters and 801M FLOPs, which is far lower than the calculation cost of face forgery detection based on large model, and achieves better detection results.