arXiv AI June 9, 2026 By Jiajian Huang, Dongliang Zhu, Zitong YU, Hui Ma, Jiayu Zhang, Chunmei Zhu, Xiaochun Cao

DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning

arXiv:2603. 23916v3 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 18

ThinkDeception: A Progressive Reinforcement Learning Framework for Interpretable Multimodal Deception Detection

arXiv:2606. 18988v1 Announce Type: new Abstract: Multimodal deception detection is critical for identifying fraudulent intentions, yet existing approaches predominantly rely on end to end black--box paradigms.

By Jinhao Song, Shan Liang, Yiqun Yue, Zhuhuayang Zhang, Tianqi Gao

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 2

CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection

arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.

By Huidong Feng, Wentao Chen, Jie Chen, Xinqi Cai, Ruolong Ma, Yinglin Zheng, Yuxin Lin, Ming Zeng

llms diffusion multimodal benchmarks

arXiv AI

Aug 11

Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation

arXiv:2608. 08009v1 Announce Type: cross Abstract: Fake news increasingly relies on cross-modal image-text forgeries, making transparent and verifiable reasoning chains an urgent need for Detecting and Grounding Multi-Modal Media Manipulation (DGM4).

By Yichun Yeh, Yiheng Li, Xiaobo Hu, Zhen Lei, Yang Yang

llms robotics multimodal benchmarks

arXiv AI

Aug 10

From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos

arXiv:2608. 06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage.

By Yifeng Luo, Yupeng Li, Liang Lan, Tian Wang

llms diffusion fine-tuning benchmarks safety

arXiv AI

Aug 10

Multi-Agent Forensic Reasoning for Generalizable Deepfake Video Detection

arXiv:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.

By Xuechao Zou, Shun Zhang, Kai Li, Yi Zhou, Xinyu Sun, Yuhui Chen, Zhe Wu, Congyan Lang, Junliang Xing

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.

By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao

llms rag multimodal benchmarks safety

Related stories

ThinkDeception: A Progressive Reinforcement Learning Framework for Interpretable Multimodal Deception Detection

CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection

Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation

From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos

Multi-Agent Forensic Reasoning for Generalizable Deepfake Video Detection

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models