Query-Based Audio-Visual Temporal Forgery Localization with Register-Enhanced Representation Learning

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.