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:2609.36145v1 Announce Type: new
Abstract: Tampered Text Detection (TTD) is essential for safeguarding document authenticity in security-critical workflows. Existing expert models are effective...
By Kaiqing Lin, Songze Li, Shen Chen, Yunfei Guo, Xiaoye Qiu, Haodong Li, Taiping Yao, Bo Wang, Youchang Xiao, Bin Li, Shouhong Ding
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
The paper introduces MoE-JEPA, a dual‑stream deepfake detection model that combines a V‑JEPA backbone with a Residual Mixture‑of‑Experts mechanism and a noise stream branch. It further incorporates a Gated Attention Multiple Instance Learning module to refine spatial semantic understanding. On the SID‑Set benchmark, MoE‑JEPA achieves a new state‑of‑the‑art accuracy of 95.54%, outperforming much larger models.
By Simone Teglia, Irene Amerini
arXiv:2608.20882v1 Announce Type: new
Abstract: Modern generators faithfully model macroscopic semantics, producing synthetic images that appear highly realistic. Consequently, decisive forensic cues...
By Haozhen Yan, Ruoxin Chen, Jiahui Zhan, Bo Wang, Youchang Xiao, Shouhong Ding, Liqing Zhang, Taiping Yao, Jianfu Zhang
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:2605.31192v2 Announce Type: replace
Abstract: Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation trac...
By Benedikt Hopf, Zongwei Wu, Radu Timofte
FUSED is a new framework that jointly detects and localizes AI-generated inpainting by combining low-level forensic cues with high-level semantic features through a sparsely-gated Mixture-of-Experts architecture. It predicts both an image-level manipulation score and a pixel-level mask of the inpainted region. On the OpenSDID cross-generator benchmark, FUSED outperforms existing methods, especially on unseen generators, and transfers effectively to the AutoSplice and CocoGlide benchmarks, doubling localization performance.
By Anton Nuzhdin, Marcel Worring, Ivona Najdenkoska
arXiv:2606. 26552v1 Announce Type: cross Abstract: The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread dissemination of highly realistic AI-generated images.
By Yangjun Wu, Keyu Yan, Yu Liu, Jingren Zhou, Fei Huang, Rong Zhang, Zhou Zhao, Fei Wu
Forensic Twins introduces a Self‑Supervised Residual Learning (SSRL) framework that trains on real images only, using a frozen forensic residual extractor to generate two disjoint crops per image. The pretext task suppresses semantic content, focusing the model on the stationary fingerprint of the image acquisition pipeline, and achieves 56.61% accuracy in attributing AI‑generator sources, outperforming prior zero‑shot methods. When combined with an offline Gaussian Mixture Model, the approach reaches 97.99% AUC across 27 unseen AI generators, including GANs, diffusion models, and commercial systems.
By Javier Mu\~noz-Haro, Ruben Tolosana, Ruben Vera-Rodriguez, Aythami Morales, Julian Fierrez
arXiv:2610.01544v1 Announce Type: new
Abstract: Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable for...
By Bingjian Yang, Shilei Zhao, Zheng Wang
arXiv:2608.17351v2 Announce Type: replace
Abstract: Open-world face anti-spoofing must address both covariate and semantic shifts: source and target domains differ in imaging conditions, while target...
By Fangling Jiang, Qi Li, Bing Liu, Weining Wang, Quilin Huang, Zhenan Sun, Ming-Hsuan Yang