MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization

Driven by Artificial Intelligence-Generated Content (AIGC),the authenticity of audio-visual content is facing severe challenges. Tem-poral Forgery Localization (TFL) aims to precisely identify manipulatedsegments within untrimmed sequences. However, existing methods arelimited by CNNs’ local receptive fields or Transformers’ quadratic com-plexity, while emerging linear models often struggle to balance globalauthentic context compression with local abrupt forgery perception. Toaddress this, we propose MG-RWKV, a multi-granularity framework thatleverages the data-dependent state evolution of RWKV to achieve ef-ficient full-sequence processing with O(T ) complexity. Our frameworkfeatures three core innovations: (1) a Bidirectional RWKV architec-ture that captures bidirectional temporal contexts without quadraticoverhead; (2) a Multi-Granularity Mixture of Experts (MG-MoE) that performs dynamic routing over explicit temporal recep-tive fields, adaptively selecting granularities based on forgery dura-tion to significantly enhance decision interpretability; and (3) Cross-Granularity Consistency (CGC), which aligns adjacent feature pyra-mid levels through hierarchical scale-wise pairing and spatial boundary-aware weighting, effectively reducing false positives in authentic regions.Extensive experiments on Lav-DF, TVIL, and Psynd datasets demon-strate that MG-RWKV achieves state-of-the-art performance with lowcomputational cost.