Structural–Semantic Perception for Diffusion-Guided Temporal Forgery Localization

Temporal Forgery Localization (TFL) is crucial for enhancing the interpretability and accountability of deepfake forensics by precisely pinpointing the manipulated segments.However, existing methods face two limitations: (1) localization precision, where one-shot boundary prediction models fail to rectify inherent initial prediction biases, and temporal emphasis overlooks modality-internal semantic forgery cues, resulting in noise-sensitive localization, and (2) cross-dataset generalization, where fixed-scale temporal receptive fields struggle to accommodate varying manipulation durations across real-world scenarios. To address these challenges, we propose a unified framework based on structural–semantic perception and diffusion-guided refinement. The structural–semantic perception comprises two complementary components: (1) structural perception, which adaptively models manipulation durations across varying temporal spans using a designed scale weight allocation network, and (2) semantic perception, which analyzes the semantic consistency within each modality through intra-modal distillation.In this way, it first suppresses low-quality forgery localization proposals, yielding a structurally and semantically reliable candidate set. Then a diffusion-based regression head further iteratively refines the candidates into precise and temporally coherent boundary trajectories.Extensive experiments on multiple TFL benchmarks demonstrate that our method achieves state-of-the-art performance.