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The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection, which remains challenging as models evolve. We introduce AIGC-text-bank, a…

Artificial Intelligence · Computer Science 2026-04-22 Zhao Wang , Max Xiong , Jianxun Lian , Zhicheng Dou

Recent years have witnessed remarkable progress in image generation task, where users can create visually astonishing images with high-quality. However, existing text-to-image diffusion models are proficient in generating concrete concepts…

Computer Vision and Pattern Recognition · Computer Science 2024-01-10 Jingyuan Yang , Jiawei Feng , Hui Huang

The generation of high-quality images has become widely accessible and is a rapidly evolving process. As a result, anyone can generate images that are indistinguishable from real ones. This leads to a wide range of applications, including…

Computer Vision and Pattern Recognition · Computer Science 2024-07-12 Sergey Sinitsa , Ohad Fried

Generative image models have emerged as a promising technology to produce realistic images. Despite potential benefits, concerns grow about its misuse, particularly in generating deceptive images that could raise significant ethical, legal,…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Jinbin Huang , Chen Chen , Aditi Mishra , Bum Chul Kwon , Zhicheng Liu , Chris Bryan

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated image detection methods suffer from performance drop when faced…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Zheling Meng , Bo Peng , Jing Dong , Tieniu Tan

The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to public information security. Although existing AIGI…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Ziyin Zhou , Yunpeng Luo , Yuanchen Wu , Ke Sun , Jiayi Ji , Ke Yan , Shouhong Ding , Xiaoshuai Sun , Yunsheng Wu , Rongrong Ji

As deep learning technology continues to evolve, the images yielded by generative models are becoming more and more realistic, triggering people to question the authenticity of images. Existing generated image detection methods detect…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Xiuli Bi , Bo Liu , Fan Yang , Bin Xiao , Weisheng Li , Gao Huang , Pamela C. Cosman

This paper explores the burgeoning field of 3D content generation within the landscape of Artificial Intelligence Generated Content (AIGC) and large-scale models. It investigates innovative methods like Text-to-3D and Image-to-3D, which…

Graphics · Computer Science 2024-05-27 Ke Zhao , Andreas Larsen

In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Most existing…

Computer Vision and Pattern Recognition · Computer Science 2019-04-03 Minfeng Zhu , Pingbo Pan , Wei Chen , Yi Yang

Text-to-image retrieval is a fundamental task in multimedia processing, aiming to retrieve semantically relevant cross-modal content. Traditional studies have typically approached this task as a discriminative problem, matching the text and…

Multimedia · Computer Science 2024-07-25 Yongqi Li , Hongru Cai , Wenjie Wang , Leigang Qu , Yinwei Wei , Wenjie Li , Liqiang Nie , Tat-Seng Chua

The rapid progress in generative models has given rise to the critical task of AI-Generated Content Stealth (AIGC-S), which aims to create AI-generated images that can evade both forensic detectors and human inspection. This task is crucial…

Computer Vision and Pattern Recognition · Computer Science 2024-08-13 Ziyin Zhou , Ke Sun , Zhongxi Chen , Huafeng Kuang , Xiaoshuai Sun , Rongrong Ji

With the rapid advancements in Artificial Intelligence Generated Image (AGI) technology, the accurate assessment of their quality has become an increasingly vital requirement. Prevailing methods typically rely on cross-modal models like…

Computer Vision and Pattern Recognition · Computer Science 2025-07-17 Qiang Li , Qingsen Yan , Haojian Huang , Peng Wu , Haokui Zhang , Yanning Zhang

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated…

Computer Vision and Pattern Recognition · Computer Science 2019-10-04 Lakshmanan Nataraj , Tajuddin Manhar Mohammed , Shivkumar Chandrasekaran , Arjuna Flenner , Jawadul H. Bappy , Amit K. Roy-Chowdhury , B. S. Manjunath

The accelerating advancement of generative models has introduced new challenges for detecting AI-generated images, especially in real-world scenarios where novel generation techniques emerge rapidly. Existing learning paradigms are likely…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Qinghui He , Haifeng Zhang , Xiuli Bi , Bo Liu , Chi-Man Pun , Bin Xiao

With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-synthesized images are now of such high quality that humans can hardly distinguish them from real images. It is imperative for media forensics to develop…

Computer Vision and Pattern Recognition · Computer Science 2022-11-24 Yan Ju , Shan Jia , Lipeng Ke , Hongfei Xue , Koki Nagano , Siwei Lyu

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 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

In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have…

Computation and Language · Computer Science 2024-05-06 Xiaomin Yu , Yezhaohui Wang , Yanfang Chen , Zhen Tao , Dinghao Xi , Shichao Song , Simin Niu , Zhiyu Li

Recent years have witnessed some exciting developments in the domain of generating images from scene-based text descriptions. These approaches have primarily focused on generating images from a static text description and are limited to…

Computer Vision and Pattern Recognition · Computer Science 2019-05-10 Gaurav Mittal , Shubham Agrawal , Anuva Agarwal , Sushant Mehta , Tanya Marwah

Distinguishing between real and AI-generated images, commonly referred to as 'image detection', presents a timely and significant challenge. Despite extensive research in the (semi-)supervised regime, zero-shot and few-shot solutions have…

Computer Vision and Pattern Recognition · Computer Science 2025-04-23 Jonathan Brokman , Amit Giloni , Omer Hofman , Roman Vainshtein , Hisashi Kojima , Guy Gilboa