MLC: Enhanced Deepfake Detection Through Multi‐Level Collaborations
Linlin Wang, Jingwei Li, Weidan Yan, Xinyu Wang, Dengyin Zhang · IET Image Processing · 2026
ABSTRACT Deepfake detection, as a defence against AI‐generated faces, has attracted significant attention. Existing image‐level detectors aim to mine forged traces in latent codes after pre‐trained backbones. However, merely considering such semantic‐level clues is often insufficient when confronted with unseen manipulations and datasets, where more complicated forgeries are encountered. To this end, this paper proposes a Multi‐Level Collaborations strategy, termed MLC, to enhance generalisation through simultaneously extracting pixel‐level fine‐grained, region‐level facial layout, and semantic‐level deep clues at different stages of encoding. Specifically, in the shallow stage, deformable convolutions with small receptive fields but adaptability, attached with spatial attentions, are used for spatial fine‐grained falsifies. In the middle stage, multiple dilated convolutions with different dilations in a pyramidal manner, further dynamically capture local incoordination within deepfakes. Finally, latent codes cooperated with such fine‐grained features, facilitate comprehensive discriminability via the long‐sequence dependency modelling system xLSTM. Moreover, multi‐task learning is employed for more stable multi‐level training. Extensive experiments show that MLC achieves superior performance compared to existing methods in both cross‐dataset and cross‐manipulation tests. The codes are available at: https://github.com/yanwd628/MLC .