Shufan Peng, Manchun Cai*, Tianliang Lu, Xiaowen Liu
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4025-4045, 2023, DOI:10.32604/cmc.2023.036688
Abstract Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns. Despite the considerable progress in existing methods, we note that: Previous works overlooked fine-grain forgery cues with high transferability. Such cues positively impact the model’s accuracy and generalizability. Moreover, single-modality often causes overfitting of the model, and Red-Green-Blue (RGB) modal-only is not conducive to extracting the more detailed forgery traces. We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues. First, we propose two functional modules to reveal and locate the deeper forged features. Our method locates… More >
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