TS-KFNet: Key-Frame Optimized Lightweight Video Forgery Detection

With the rapid development of generative artificial intelligence technologies, video forgery techniques have evolved from localized facial replacement to multimodal scene synthesis text to video , posing severe challenges to media authenticity. Existing detection methods struggle to meet the requirements for identifying high-quality synthetic videos due to insufficient spatiotemporal dependency modeling, low computational efficiency, and limited sensitivity to subtle local artifacts. To address this, we propose TS-KFNet—a lightweight dual-stream detection framework that achieves efficient video forgery detection by fusing global spatiotemporal attention with keyframe-based local artifact analysis. The framework adopts TimeSformer backbone network to capture global motion and appearance consistency through a divided space-time attention mechanism, reducing computational complexity from $O TN^2 $ to $O T^2_N^2 $. A dynamic keyframe selection strategy is introduced to filter the top 10 most informative keyframes based on motion-compensated grayscale difference analysis, significantly reducing computational costs. Simultaneously, a CNN-enhanced branch extracts local artifact features from keyframes, forming a hybrid architecture that balances efficiency and accuracy. Experiments on 8 cutting-edge video generation models demonstrate that TS-KFNet achieves an average accuracy of 94.0 and AUC of 99.0 , outperforming existing methods by up to 12.5 in accuracy improvement. The inference speed is 10 times faster than the state-of-the-art method AIGVDet. The core contributions include a multi-granularity detection paradigm, a keyframe-based efficient inference framework, and an evaluation benchmark for emerging forgery technologies. This study provides a reliable solution for real-time high-precision long video forgery detection in dynamic complex scenarios.