FORGE is a forensic deepfake analysis system that provides region‑grounded natural language explanations for image manipulations. It addresses the inductive bias mismatch of multimodal large language models by adding a Vision‑Only Model trained on dense patch prediction, allowing the language model to interleave tokens with preserved spatial correspondence. Across face‑manipulated and fully synthetic content, FORGE delivers fine‑grained attribute queries and outperforms in‑domain baselines, with region‑specific evaluation and human studies confirming explanation faithfulness.
By Rohit Kundu, Shan Jia, Vishal Mohanty, Athula Balachandran, Amit K. Roy-Chowdhury
arXiv:2609.38391v1 Announce Type: new
Abstract: Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meani...
By Kirill Koltsov, Aleksandr Gushchin, Dmitriy Vatolin, Anastasia Antsiferova
arXiv:2606. 15880v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding.
By Kaiqing Lin, Zhiyuan Yan, Ruoxin Chen, Ke-Yue Zhang, Yue Zhou, Caiyong Piao, Bin Li, Taiping Yao, Bo Wang, Youchang Xiao, Shouhong Ding
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
arXiv:2609.39066v1 Announce Type: new
Abstract: Conventional image forgery detection methods produce binary scores or pixel-level masks without interpretable evidence, while recent multimodal large l...
By Zhiya Tan, Jing Huang, Changtao Miao, Lin Tan, Xin Zhang, Weiwei Feng, Jianshu Li, Joey Tianyi Zhou
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
ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.
By Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma, Conghui He, Weijia Li
The paper investigates an agentic framework for open‑world fake image detection that combines specialist detectors with per‑detector triage, prompting, and conflict‑aware evidence arbitration. Experiments across six configurations and three multimodal large language model backbones reveal that naive detector fusion yields high false‑positive rates, while triage and prompting consistently filter unreliable evidence. The most significant improvement comes from the reasoning component: a stronger judge markedly outperforms a weaker one, especially under distribution shift, and overall manipulation recall is nearly saturated, highlighting that the key challenge lies in calibrating trust and arbitrating conflicting forensic evidence rather than detecting manipulations themselves.
By Xianlong Li (IMT School for Advanced Studies Lucca, Italy), Pietro Bongini (University of Siena, Italy), Niccol\'o Pancino (University of Siena, Italy), Marco Blanchini (IMT School for Advanced Studies Lucca, Italy), Benedetta Tondi (University of Siena, Italy), Mauro Barni (University of Siena, Italy)
arXiv:2411. 19715v4 Announce Type: replace-cross Abstract: We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector.
By Xinjie Cui, Yuezun Li, Delong Zhu, Jiaran Zhou, Junyu Dong, Siwei Lyu
arXiv:2512. 16300v3 Announce Type: replace Abstract: Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language models (MLLMs) with high-level semantic knowledge.
By Fanrui Zhang, Qiang Zhang, Sizhuo Zhou, Jianwen Sun, Chuanhao Li, Jiaxin Ai, Yukang Feng, Yujie Zhang, Wenjie Li, Zizhen Li, Yifan Chang, Jiawei Liu, Kaipeng Zhang
The paper introduces an evidence‑guided detector‑localizer‑reasoner system for text‑centric image forensics, addressing the challenges posed by AI‑generated content. It combines an image‑level authenticity detector, a localizer that extracts tampered regions, and an MLLM‑based reasoner that generates structured forensic reports grounded in the detected evidence. The system employs iterative difficulty‑aware mining and report‑mask consistency post‑processing, achieving a score of 0.638 and ranking second in the ACM Multimedia 2026 GenText‑Forensics Challenge.
By Peifeng Liu, Bin Li, Qingsong Zhang, Yangxin Yu, Leqing Chen, Xiaoye Qiu
arXiv:2604. 02694v2 Announce Type: replace-cross Abstract: The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safety.
By Fanwei Zeng, Changtao Miao, Jing Huang, Zhiya Tan, Shutao Gong, Xiaoming Yu, Yang Wang, Weibin Yao, Joey Tianyi Zhou, Jianshu Li, Ying Yan