关键词: 深度伪造检测, 时序伪造定位, 状态空间模型, 多模态融合, 边界回归精化

Abstract: To address the quadratic computational complexity of self-attention architectures and the neglect of the "audio-driven vision" generative causal logic under symmetric equal-weight interaction in existing audio-visual temporal forgery localization, a localization method centered on a Cross-Modal Selective State Space Model (Cross-Mamba) was proposed. Firstly, an asymmetric coupling mechanism was designed at the state-dynamics level, where auditory priors dynamically modulated the time-varying step size of visual features, and boundary-aware gating achieved cross-modal alignment and mismatched-state reset. Secondly, a Dual-Stage Relative Boundary Refinement (DS-RBR) head was constructed, transforming traditional coarse-grained anchor regression into continuous sub-interval offset refinement to handle the boundary uncertainty of extremely short tampered segments. At the strictest temporal Intersection over Union (tIoU) threshold of 0.95, the proposed method achieved an Average Precision (AP) of 49.2% on the LAV-DF benchmark, outperforming the previous best model AuViRe (46.5%) by 2.7 percentage points, and 14.3% on the million-scale open benchmark AV-Deepfake1M, exceeding AuViRe (11.7%) by 2.6 percentage points. Experimental results show that the method overcomes the computational bottleneck of long-video modeling with linear complexity, mitigates the quantization error of extremely short tampered segments, and maintains stable boundary localization performance in highly degraded and open scenarios.

Key words: deepfake detection, Temporal Forgery Localization (TFL), State Space Model (SSM), multimodal fusion, boundary regression refinement