Word-Anchored Temporal Forgery Localization
Tianyi Wang, Xi Shao, Harry Cheng, Yinglong Wang, Mohan Kankanhalli

TL;DR
This paper introduces WAFL, a novel word-anchored approach for temporal forgery localization that improves accuracy and efficiency by shifting from continuous to discrete word-level classification, utilizing a forensic feature realignment module and a specialized loss function.
Contribution
The paper proposes a new paradigm for TFL based on word-level classification, a forensic feature realignment module, and an artifact-centric asymmetric loss, advancing the state-of-the-art in forgery localization.
Findings
WAFL outperforms existing methods in localization accuracy.
It requires fewer parameters and is computationally efficient.
Effective in both in- and cross-dataset evaluations.
Abstract
Current temporal forgery localization (TFL) approaches typically rely on temporal boundary regression or continuous frame-level anomaly detection paradigms to derive candidate forgery proposals. However, they suffer not only from feature granularity misalignment but also from costly computation. To address these issues, we propose word-anchored temporal forgery localization (WAFL), a novel paradigm that shifts the TFL task from temporal regression and continuous localization to discrete word-level binary classification. Specifically, we first analyze the essence of temporal forgeries and identify the minimum meaningful forgery units, word tokens, and then align data preprocessing with the natural linguistic boundaries of speech. To adapt powerful pre-trained foundation backbones for feature extraction, we introduce the forensic feature realignment (FFR) module, mapping representations…
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Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Misinformation and Its Impacts
