Bridging Pixels and Words: Mask-Aware Local Semantic Fusion for Multimodal Media Verification
Chen, Zizhao, Wei, Ping, Ren, Ziyang, Li, Huan, Yin, Xiangru
原文摘要
As the harm caused by fake news grows, the task of detecting and grounding multi-modal media manipulation (DGM4) is gaining more attention. Existing multimodal methods overlook fine-grained semantic alignment between visual and textual modalities, thereby limiting their ability to detect sophisticated and subtle cross-modal manipulations. To address this challenge, we present MaLSF, a novel Mask-aware Local Semantic Fusion framework that explicitly bridges words and pixels via mask-label pairs, enabling the model to perform precise reasoning over fine-grained cross-modal correspondences. MaLSF captures cross-modal local semantics through two key innovations: 1) A Bidirectional Cross-modal Verification Module (BCV) that identifies semantic conflicts between masked regions and associated labels via a bidirectional query mechanism; 2) A Hierarchical Semantic Aggregation (HSA) Module that adaptively aggregates multi-granularity local semantics into decoupled features for task-specific verification. In addition, to extract fine-grained mask-label pairs, we introduce a set of diverse mask-label pair extraction parsers. The proposed model is evaluated on multiple datasets and achieves state-of-the-art performance on both the DGM4 and multimodal fake news detection tasks. Extensive ablation studies and visualization results further verify its effectiveness and interpretability.
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表