Rethinking Temporal Forgery Localization: From Semantic-Oriented Dense Detection to Forgery-Sensitive Sparse Localization

Temporal Forgery Localization (TFL) aims to identify the temporal boundaries of manipulated segments within otherwise authentic content. Given the commonalities between TFL and Temporal Action Localization (TAL), many TFL methods build on TAL-derived feature modeling and localization designs, with further forgery-specific adaptations. However, these adaptations often address representation learning and localization separately, leaving the preservation of manipulation-sensitive responses and the modeling of sparse interval structure insufficiently explored jointly. We propose DiST-TFL, a Diffusion-guided Sparse Temporal Forgery Localization framework that jointly adapts representation learning and temporal localization to TFL-specific characteristics. Diffusion-based feature refinement provides a reference for revealing manipulation-sensitive responses, which are propagated across scales through a hierarchical temporal feature pyramid. A sparse query-based localization module then predicts a compact set of forged intervals without Non-Maximum Suppression (NMS). Experiments on LAV-DF and AV-Deepfake1M demonstrate substantial improvements in overall localization performance, while ablation studies validate the complementary roles of diffusion-guided feature modeling and sparse localization.