Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments in video and audio, offering strong interpretability for security and forensics. While recent State Space Models (SSMs) show promise in precise temporal reasoning, their use in TFL is hindered by ambiguous boundaries, sparse forgeries, and limited long-range modeling. We propose DeformTrace, which enhances SSMs with deformable dynamics and relay mechanisms to address these challenges. Specifically, Deformable Self-SSM (DS-SSM) introduces dynamic receptive fields into SSMs for precise temporal localization. To further enhance its capacity for temporal reasoning and mitigate long-range decay, a Relay Token Mechanism is integrated into DS-SSM. Besides, Deformable Cross-SSM (DC-SSM) partitions the global state space into query-specific subspaces, reducing non-forgery information accumulation and boosting sensitivity to sparse forgeries. These components are integrated into a hybrid architecture that combines the global modeling of Transformers with the efficiency of SSMs. Extensive experiments show that DeformTrace achieves state-of-the-art performance with fewer parameters, faster inference, and stronger robustness.

Temporal forgery in multimedia-where audio or video streams are subtly manipulated-poses critical challenges for content authenticity verification. While video-level detection has advanced, Temporal Forgery Localization (TFL) remains underexplored, often limited by weak audio-visual modeling and reliance on non-learnable post-processing. To address these challenges, we propose RegQAV, a Register-enhanced Query-based Audio-Visual framework for TFL. RegQAV exploits pretrained foundation models to capture fine-grained audio-visual correspondences and learnable registers are introduced to mitigate the model's tendency to overly focus on a limited set of temporal features. A query-based localization strategy enables end-to-end optimization without post-processing. We also introduce a Modality Fusion Adapter (MFA) for effective multi-scale integration of audio-visual data, a Deepfake Queries Generation (DQG) module for efficient query initialization, and a Poisson Count-Based Approach to dynamically predict the number of forgeries. Experiments on LAV-DF and AV-Deepfake1M show that RegQAV achieves state-of-the-art performance with fewer parameters, faster inference, and stronger generalization. This work offers significant potential for real-time deepfake detection and other multimedia verification applications. The code is available at https://github.com/zxd3099/RegQAV.