AAAI 2026technical0 citations
Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach
Lvpan Cai, Haowei Wang, Jiayi Ji, Yanshu Zhoumen, Shen Chen, Taiping Yao, Xiaoshuai Sun
Abstract
The rise of AI-generated image tools has made localized forgeries increasingly realistic, posing challenges for visual content integrity. Although recent efforts have explored localized AIGC detection, existing datasets predominantly focus on object-level forgeries while overlooking broader scene edits in regions such as sky or ground. To address these limitations, we introduce BR-Gen, a large-scale dataset of 150,000 locally forged images with diverse scene-aware annotations, which are based on semantic calibration to ensure high-quality samples. BR-Gen is constructed through a fully automated ``Perception-Creation-Evaluation
BibTeX
@inproceedings{aaai2026_zoominginonfakes,
title = {Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach},
author = {Lvpan Cai and Haowei Wang and Jiayi Ji and Yanshu Zhoumen and Shen Chen and Taiping Yao and Xiaoshuai Sun},
booktitle = {AAAI 2026},
year = {2026}
}