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arXiv · 2605.23113

Inconsistency-aware Multimodal Schr\"odinger Bridge for Deepfake Localization

Jiayu Xiong·Jing Wang·Qi Zhang·Wanlong Wang·Jun Xue
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Abstract

Audio-visual deepfake localization demands interval-level outputs that serve as temporal evidence. Despite recent progress, symmetric fusion under single-sided or asynchronous forgeries propagates cross-modal noise, degrading high-precision localization. We present IaMSB, an inconsistency-aware multimodal Schr\"odinger Bridge (SB) that jointly estimates cross-modal consistency and performs interval-level localization. Unlike diffusion models, SB minimizes path-distribution discrepancy and yields consistency scores without explicit noise injection or denoising. With the Schr\"odinger Bridge (SB), IaMSB unifies consistency estimation, cross-modal information selection, and bridge-step scheduling in one framework. Specifically, a lightweight coarse bridge first proposes candidate intervals and estimates cross-modal consistency; these statistics select cross-modal witness signals and allocate bridge steps asymmetrically across modalities. A refinement bridge then performs step-tuned fusion and outputs refined, time-aligned intervals. IaMSB anticipates single-sided and asynchronous forgeries and, using bottlenecked cross-modal interaction with step allocation, suppresses noise transfer, avoids unnecessary iterations. Across benchmarks, IaMSB stabilizes strict-IoU boundary precision, raising AP@0.95 by 3%~10%, and yields improved high-precision localization, particularly for single-sided forgeries.

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Jiayu Xiong, Jing Wang, Qi Zhang, Wanlong Wang, Jun Xue. 2026-05-22. Inconsistency-aware Multimodal Schr\"odinger Bridge for Deepfake Localization. https://arxiv.org/abs/2605.23113

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