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The rapid advancement of generative AI has revolutionized image creation, enabling high-quality synthesis from text prompts while raising critical challenges for media authenticity. We present Ai-GenBench, a novel benchmark designed to…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Lorenzo Pellegrini , Davide Cozzolino , Serafino Pandolfini , Davide Maltoni , Matteo Ferrara , Luisa Verdoliva , Marco Prati , Marco Ramilli

The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading to the demand for detectors capable of distinguishing between AI-generated fake…

Computer Vision and Pattern Recognition · Computer Science 2023-06-27 Mingjian Zhu , Hanting Chen , Qiangyu Yan , Xudong Huang , Guanyu Lin , Wei Li , Zhijun Tu , Hailin Hu , Jie Hu , Yunhe Wang

Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research…

Computer Vision and Pattern Recognition · Computer Science 2023-02-27 Md Awsafur Rahman , Bishmoy Paul , Najibul Haque Sarker , Zaber Ibn Abdul Hakim , Shaikh Anowarul Fattah

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face…

Computer Vision and Pattern Recognition · Computer Science 2024-02-15 Yuhang Lu , Touradj Ebrahimi

Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessitating reliable detection methods. However, two key…

Computer Vision and Pattern Recognition · Computer Science 2026-01-19 Long Ma , Zihao Xue , Yan Wang , Zhiyuan Yan , Jin Xu , Xiaorui Jiang , Haiyang Yu , Yong Liao , Zhen Bi

Recent generative models show impressive performance in generating photographic images. Humans can hardly distinguish such incredibly realistic-looking AI-generated images from real ones. AI-generated images may lead to ubiquitous…

Computer Vision and Pattern Recognition · Computer Science 2024-03-08 Nan Zhong , Yiran Xu , Sheng Li , Zhenxing Qian , Xinpeng Zhang

How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments required for user studies, many researchers have turned towards…

Computer Vision and Pattern Recognition · Computer Science 2025-02-10 Zicheng Zhang , Haoning Wu , Chunyi Li , Yingjie Zhou , Wei Sun , Xiongkuo Min , Zijian Chen , Xiaohong Liu , Weisi Lin , Guangtao Zhai

One of the key challenges of detecting AI-generated images is spotting images that have been created by previously unseen generative models. We argue that the limited diversity of the training data is a major obstacle to addressing this…

Computer Vision and Pattern Recognition · Computer Science 2025-06-11 Jeongsoo Park , Andrew Owens

Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence…

Artificial Intelligence · Computer Science 2023-09-26 Zeyu Lu , Di Huang , Lei Bai , Jingjing Qu , Chengyue Wu , Xihui Liu , Wanli Ouyang

As image generation technology advances, AI-based image generation has been applied in various fields and Artificial Intelligence Generated Content (AIGC) has garnered widespread attention. However, the development of AI-based image…

Computer Vision and Pattern Recognition · Computer Science 2023-11-30 Jiquan Yuan , Xinyan Cao , Changjin Li , Fanyi Yang , Jinlong Lin , Xixin Cao

With the rapid development of generative models, discerning AI-generated content has evoked increasing attention from both industry and academia. In this paper, we conduct a sanity check on "whether the task of AI-generated image detection…

Computer Vision and Pattern Recognition · Computer Science 2025-02-18 Shilin Yan , Ouxiang Li , Jiayin Cai , Yanbin Hao , Xiaolong Jiang , Yao Hu , Weidi Xie

Modern multimodal generators can now produce scientific figures at near-publishable quality, creating a new challenge for visual forensics and research integrity. Unlike conventional AI-generated natural images, scientific figures are…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 You Hu , Chenzhuo Zhao , Changfa Mo , Haotian Liu , Xiaobai Li

Artificial Intelligence (AI)-generated images have become increasingly realistic and readily adaptable to concrete real-world claims, creating new challenges for verifying visual evidence. A concrete emerging risk is AI-generated refund…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Xinyu Yan , Boyang Chen , Jiaming Zhang , Tiantong Wu , Hong Xi Tae , Yichen He , Tiantong Wang , Yachun Mi , Yurong Hao , Yilei Zhao , Lei Xiao , Longtao Huang , Pengjun Xie , Wei Liu , Wei Yang Bryan Lim

With the rapid proliferation of image generative models, the authenticity of digital images has become a significant concern. While existing studies have proposed various methods for detecting AI-generated content, current benchmarks are…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Huixuan Zhang , Xiaojun Wan

The rapid advancement of generative AI has raised concerns about the authenticity of digital images, as highly realistic fake images can now be generated at low cost, potentially increasing societal risks. In response, several datasets have…

Computer Vision and Pattern Recognition · Computer Science 2026-02-12 Hanzhe Yu , Yun Ye , Jintao Rong , Qi Xuan , Chen Ma

Recent generative models produce images with a level of authenticity that makes them nearly indistinguishable from real photos and artwork. Potential harmful use cases of these models, necessitate the creation of robust synthetic image…

Computer Vision and Pattern Recognition · Computer Science 2025-01-15 Delyan Boychev , Radostin Cholakov

The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a binary classification task. In this work, the deepfake…

Computer Vision and Pattern Recognition · Computer Science 2023-03-02 Luca Guarnera , Oliver Giudice , Sebastiano Battiato

With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media. However, due to the significant variance in quality among different AIGIs, there…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Chunyi Li , Tengchuan Kou , Yixuan Gao , Yuqin Cao , Wei Sun , Zicheng Zhang , Yingjie Zhou , Zhichao Zhang , Weixia Zhang , Haoning Wu , Xiaohong Liu , Xiongkuo Min , Guangtao Zhai

\underline{AI} \underline{G}enerated \underline{C}ontent (\textbf{AIGC}) has gained widespread attention with the increasing efficiency of deep learning in content creation. AIGC, created with the assistance of artificial intelligence…

Computer Vision and Pattern Recognition · Computer Science 2023-03-23 Zicheng Zhang , Chunyi Li , Wei Sun , Xiaohong Liu , Xiongkuo Min , Guangtao Zhai

With the rapid advancement of generative models, the realism of AI-generated images has significantly improved, posing critical challenges for verifying digital content authenticity. Current deepfake detection methods often depend on…

Computer Vision and Pattern Recognition · Computer Science 2025-06-04 Jiarui Wang , Huiyu Duan , Juntong Wang , Ziheng Jia , Woo Yi Yang , Xiaorong Zhu , Yu Zhao , Jiaying Qian , Yuke Xing , Guangtao Zhai , Xiongkuo Min
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