CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization

• URL: http://arxiv.org/abs/2602.02175v2
• Date: Tue, 03 Feb 2026 04:22:27 GMT
• Title: CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization
• Authors: Xinquan Yu, Wei Lu, Xiangyang Luo, Rui Yang,
• Abstract summary: Coupling Implicit and Explicit Cues (CIEC) aims to achieve multimodal weakly-supervised manipulation localization for image-text pairs.<n>It integrates forgery cues from both visual and textual perspectives to lock onto suspicious regions aided by spatial priors.<n>For the latter, we devise the Visual-deviation Calibrated Token Grounding (VCTG) module. It focuses on meaningful content words and leverages relative visual bias to assist token localization.
• Score: 25.78477436147408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To mitigate the threat of misinformation, multimodal manipulation localization has garnered growing attention. Consider that current methods rely on costly and time-consuming fine-grained annotations, such as patch/token-level annotations. This paper proposes a novel framework named Coupling Implicit and Explicit Cues (CIEC), which aims to achieve multimodal weakly-supervised manipulation localization for image-text pairs utilizing only coarse-grained image/sentence-level annotations. It comprises two branches, image-based and text-based weakly-supervised localization. For the former, we devise the Textual-guidance Refine Patch Selection (TRPS) module. It integrates forgery cues from both visual and textual perspectives to lock onto suspicious regions aided by spatial priors. Followed by the background silencing and spatial contrast constraints to suppress interference from irrelevant areas. For the latter, we devise the Visual-deviation Calibrated Token Grounding (VCTG) module. It focuses on meaningful content words and leverages relative visual bias to assist token localization. Followed by the asymmetric sparse and semantic consistency constraints to mitigate label noise and ensure reliability. Extensive experiments demonstrate the effectiveness of our CIEC, yielding results comparable to fully supervised methods on several evaluation metrics.

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