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The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models. Within just two years of development, it was unprecedentedly of high-quality, diversity, and…

Artificial Intelligence · Computer Science 2023-10-16 Zeqiang Lai , Xizhou Zhu , Jifeng Dai , Yu Qiao , Wenhai Wang

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP, a pipeline utilizing…

Computer Vision and Pattern Recognition · Computer Science 2024-04-04 Hsin-Ying Lee , Hung-Yu Tseng , Hsin-Ying Lee , Ming-Hsuan Yang

Text-to-image diffusion models have shown impressive capabilities in generating realistic visuals from natural-language prompts, yet they often struggle with accurately binding attributes to corresponding objects, especially in prompts…

Computer Vision and Pattern Recognition · Computer Science 2025-05-05 Do Huu Dat , Nam Hyeonu , Po-Yuan Mao , Tae-Hyun Oh

Text-to-image (T2I) generative models such as Stable Diffusion and FLUX can synthesize realistic, high-quality images directly from textual prompts. The resulting image quality depends critically on well-crafted prompts that specify both…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Mingzhe Li , Renhao Zhang , Zhiyang Wen , Siqi Pan , Bruno Castro da Silva , Juan Zhai , Shiqing Ma

Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these advances, existing models struggle with complex prompts…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Eric Hanchen Jiang , Yasi Zhang , Zhi Zhang , Yixin Wan , Andrew Lizarraga , Shufan Li , Ying Nian Wu

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a…

Computer Vision and Pattern Recognition · Computer Science 2023-10-23 Thanh Van Le , Hao Phung , Thuan Hoang Nguyen , Quan Dao , Ngoc Tran , Anh Tran

Backdoor attacks targeting text-to-image diffusion models have advanced rapidly. However, current backdoor samples often exhibit two key abnormalities compared to benign samples: 1) Semantic Consistency, where backdoor prompts tend to…

Computer Vision and Pattern Recognition · Computer Science 2025-07-25 Jie Zhang , Zhongqi Wang , Shiguang Shan , Xilin Chen

Data poisoning attacks manipulate training data to introduce unexpected behaviors into machine learning models at training time. For text-to-image generative models with massive training datasets, current understanding of poisoning attacks…

Cryptography and Security · Computer Science 2024-04-30 Shawn Shan , Wenxin Ding , Josephine Passananti , Stanley Wu , Haitao Zheng , Ben Y. Zhao

Large Language Models face security threats from jailbreak attacks. Existing research has predominantly focused on prompt-level attacks while largely ignoring the underexplored attack surface of user-controlled response prefilling. This…

Cryptography and Security · Computer Science 2025-08-27 Yakai Li , Jiekang Hu , Weiduan Sang , Luping Ma , Dongsheng Nie , Weijuan Zhang , Aimin Yu , Yi Su , Qingjia Huang , Qihang Zhou

Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. However, these methods are constrained by the need for manual…

Computer Vision and Pattern Recognition · Computer Science 2025-05-21 Yuanyuan Chang , Yinghua Yao , Tao Qin , Mengmeng Wang , Ivor Tsang , Guang Dai

In recent years, text-to-image (T2I) generation models have made significant progress in generating high-quality images that align with text descriptions. However, these models also face the risk of unsafe generation, potentially producing…

Cryptography and Security · Computer Science 2025-04-16 Huming Qiu , Guanxu Chen , Mi Zhang , Xiaohan Zhang , Xiaoyu You , Min Yang

Large language models (LLMs) have exhibited outstanding performance in engaging with humans and addressing complex questions by leveraging their vast implicit knowledge and robust reasoning capabilities. However, such models are vulnerable…

Computation and Language · Computer Science 2024-10-16 Hao Yang , Lizhen Qu , Ehsan Shareghi , Gholamreza Haffari

Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However,…

Computation and Language · Computer Science 2024-12-18 Weixiong Zheng , Peijian Zeng , Yiwei Li , Hongyan Wu , Nankai Lin , Junhao Chen , Aimin Yang , Yongmei Zhou

Large Language Models (LLMs) have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful content, causing adverse impacts across various societal…

Cryptography and Security · Computer Science 2025-12-17 Fan Yang

Text-to-Image (T2I) models generate high-quality images but are vulnerable to malicious backdoor attacks that inject harmful biases (e.g., trigger-activated gender or racial stereotypes). Existing debiasing methods, often designed for…

Computer Vision and Pattern Recognition · Computer Science 2026-03-02 Hongyi Cai , Mohammad Mahdinur Rahman , Mingkang Dong , Muxin Pu , Moqyad Alqaily , Jie Li , Xinfeng Li , Jialie Shen , Meikang Qiu , Qingsong Wen

The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work…

Computer Vision and Pattern Recognition · Computer Science 2025-04-22 Brian Nlong Zhao , Yuhang Xiao , Jiashu Xu , Xinyang Jiang , Yifan Yang , Dongsheng Li , Laurent Itti , Vibhav Vineet , Yunhao Ge

The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent, often requiring `prompt engineering'. To harness visual concepts from target images without prompt…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Shweta Mahajan , Tanzila Rahman , Kwang Moo Yi , Leonid Sigal

Recent advancements in generative AI have enabled ubiquitous access to large language models (LLMs). Empowered by their exceptional capabilities to understand and generate human-like text, these models are being increasingly integrated into…

Cryptography and Security · Computer Science 2024-10-02 Zhiyuan Yu , Xiaogeng Liu , Shunning Liang , Zach Cameron , Chaowei Xiao , Ning Zhang

Diffusion-based text-to-image models have shown immense potential for various image-related tasks. However, despite their prominence and popularity, customizing these models using unauthorized data also brings serious privacy and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Sen Peng , Jijia Yang , Mingyue Wang , Jianfei He , Xiaohua Jia

Text-to-Image generation models have revolutionized the artwork design process and enabled anyone to create high-quality images by entering text descriptions called prompts. Creating a high-quality prompt that consists of a subject and…

Cryptography and Security · Computer Science 2024-04-16 Xinyue Shen , Yiting Qu , Michael Backes , Yang Zhang
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