A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization

• URL: http://arxiv.org/abs/2507.16596v2
• Date: Mon, 04 Aug 2025 08:10:14 GMT
• Title: A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization
• Authors: Wenbo Xu, Junyan Wu, Wei Lu, Xiangyang Luo, Qian Wang,
• Abstract summary: We present a framework for weakly-supervised temporal forgery localization.<n>It aims to identify temporal partial forged segments using only video-level annotations.<n>Extensive experiments demonstrate the effectiveness of the proposed framework.
• Score: 21.13433908232578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current researches on Deepfake forensics often treat detection as a classification task or temporal forgery localization problem, which are usually restrictive, time-consuming, and challenging to scale for large datasets. To resolve these issues, we present a multimodal deviation perceiving framework for weakly-supervised temporal forgery localization (MDP), which aims to identify temporal partial forged segments using only video-level annotations. The MDP proposes a novel multimodal interaction mechanism (MI) and an extensible deviation perceiving loss to perceive multimodal deviation, which achieves the refined start and end timestamps localization of forged segments. Specifically, MI introduces a temporal property preserving cross-modal attention to measure the relevance between the visual and audio modalities in the probabilistic embedding space. It could identify the inter-modality deviation and construct comprehensive video features for temporal forgery localization. To explore further temporal deviation for weakly-supervised learning, an extensible deviation perceiving loss has been proposed, aiming at enlarging the deviation of adjacent segments of the forged samples and reducing that of genuine samples. Extensive experiments demonstrate the effectiveness of the proposed framework and achieve comparable results to fully-supervised approaches in several evaluation metrics.

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