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

arXiv: https://arxiv.org/abs/2602.02175 · HTML: https://mlsanity.com/paper/2602.02175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Scaling up GANs for Text-to-Image Synthesis](https://mlsanity.com/paper/2303.05511/) (2023)
- [An Empirical Study of Training End-to-End Vision-and-Language Transformers](https://mlsanity.com/paper/2111.02387/) (2021)
- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)
- [Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2304.02556/) (2023)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Detecting and Grounding Multi-Modal Media Manipulation and Beyond](https://mlsanity.com/paper/2309.14203/) (2023)
- [Fully Convolutional Multi-Class Multiple Instance Learning](https://mlsanity.com/paper/1412.7144/) (2014)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding](https://mlsanity.com/paper/2412.12718/) (2024)
- [Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation](https://mlsanity.com/paper/2309.09667/) (2023)
- [A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization](https://mlsanity.com/paper/2507.16596/) (2025)
- [Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding](https://mlsanity.com/paper/2309.12657/) (2023)
- [Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning Network](https://mlsanity.com/paper/2505.01880/) (2025)
- [RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection](https://mlsanity.com/paper/2412.18254/) (2024)
- [Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2506.05890/) (2025)
- [Weakly-Supervised Image Forgery Localization via Vision-Language Collaborative Reasoning Framework](https://mlsanity.com/paper/2508.01338/) (2025)
- [Fine-grained Multiple Supervisory Network for Multi-modal Manipulation Detecting and Grounding](https://mlsanity.com/paper/2508.02479/) (2025)

## Built on by (most cited first, 1 of 1)

- [Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2608.08009/) (2026)

## Abstract (arXiv)

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.