SFMFNet uses wavelet-frequency gating, token-selective cross-attention, and blur pooling to reach 0.8682 average cross-dataset AUC with only 1.27 GFLOPs and 6.64M parameters.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogn ition (CVPR), Boston, MA, USA, pp
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A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection
SFMFNet uses wavelet-frequency gating, token-selective cross-attention, and blur pooling to reach 0.8682 average cross-dataset AUC with only 1.27 GFLOPs and 6.64M parameters.