The paper introduces Multi-Level Consistency and Conflict Decoupling (MLCCD), a method for detecting fake news in multimodal content by leveraging the layered knowledge of multimodal large language models (MLLMs). MLCCD first selects relevant visual and textual tokens using the MLLM’s final decision representation, then identifies tokens whose representations change sharply across adjacent layers to capture layer‑specific knowledge. Finally, it decouples this knowledge into consistency and conflict evidence, aggregating each across layers with dedicated routers that adapt to varying conflict levels, achieving superior performance on three benchmarks.
By Kai Qian, Dongjie Xu, Weijie Shi, Jiaqi Wang, Haolun Ma, Jia Zhu, Jiajie Xu
arXiv:2606. 03066v1 Announce Type: new Abstract: The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability.
By Jinjie Shen, Yaxiong Wang, Yujiao Wu, Lechao Cheng, Tianrui Hui, Nan Pu, Zhihui Li, Zhun Zhong
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
By Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang
arXiv:2609.36850v1 Announce Type: new
Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
By Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhichao Lian
arXiv:2606. 07651v1 Announce Type: new Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims.
By Kevin Patel, Shashi Bhushan Jha
arXiv:2601. 14954v3 Announce Type: replace Abstract: Social media increasingly disseminates information through mixed image text posts, but rumors often exploit subtle inconsistencies and forged content, making detection based solely on post content difficult.
By Han Li, Hua Sun
arXiv:2608.22832v1 Announce Type: new
Abstract: The social interactions among crowds via \textit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint confli...
By Xiansheng Luo, Chaowei Zhang, Zewei Zhang, Yi Zhu, Jipeng Qiang
arXiv:2512.20257v2 Announce Type: replace
Abstract: With the rise of easily accessible generative tools for creating and manipulating multimedia content, the threat of realistic synthetic alterations...
By Daniele Cardullo, Simone Teglia, Irene Amerini
arXiv:2606. 24112v1 Announce Type: new Abstract: Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text--image framing errors.
By Chenhao Dang, Dantong Zhu, Jun Yang, Conghui He, Weijia Li
arXiv:2610.35475v1 Announce Type: new
Abstract: Automated fake news detection is vital on social media, where fake news is easily rewritten to fool detectors. However, existing approaches typically r...
By Dexin Liu, Fan Yang, Zimu Wang, Xunfei Zhu, Bolin Ren, Bingyan Zhang, Wei Wang, Qi Chen
arXiv:2512. 03553v3 Announce Type: replace-cross Abstract: Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must be timely, multimodal, and robust to evolving forms of unwanted content.
By Wei Chee Yew, Hailun Xu, Sanjay Saha, Xiaotian Fan, Hiok Hian Ong, David Yuchen Wang, Kanchan Sarkar, Zhenheng Yang, Danhui Guan
The paper introduces Head-wise Modality Decoupling and Regulation (HMDR) to improve fake news detection when some modalities (image or text) are missing. HMDR first decouples modality representations via soft head grouping based on attention preference, then regulates head contributions to balance modality attention, and finally enforces modality invariance by aligning representations when one modality is replaced. Experiments on two benchmarks demonstrate that HMDR enhances detection performance under missing modalities while preserving accuracy on complete inputs.
By Kai Qian, Dongjie Xu, Weijie Shi, Yezhao Xu, Jiaqi Wang, Hanghui Guo, Mengze Li, Hao Chen, Ziyi Liu, Jiajie Xu