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Text-to-image (T2I) models have raised increasing safety concerns due to their capacity to generate NSFW and other banned objects. To mitigate these risks, safety filters and concept removal techniques have been introduced to block…

Cryptography and Security · Computer Science 2026-01-13 Xi Ye , Yiwen Liu , Lina Wang , Run Wang , Geying Yang , Yufei Hou , Jiayi Yu

Recent advances in diffusion models have notably enhanced text-to-image (T2I) generation quality, but they also raise the risk of generating unsafe content. Traditional safety methods like text blacklisting or harmful content classification…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Zongsheng Cao , Yangfan He , Anran Liu , Jun Xie , Feng Chen , Zepeng Wang

Text-to-image (T2I) generative diffusion models have demonstrated outstanding performance in synthesizing diverse, high-quality visuals from text captions. Several layout-to-image models have been developed to control the generation process…

Computer Vision and Pattern Recognition · Computer Science 2025-02-11 Ahmad Süleyman , Göksel Biricik

Text-to-image (T2I) generative models have achieved remarkable visual fidelity, yet remain vulnerable to generating unsafe content. Existing safety defenses typically intervene internally within the generative model, but suffer from severe…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Xiangtao Meng , Yingkai Dong , Ning Yu , Li Wang , Zheng Li , Shanqing Guo

Text-to-image diffusion models (T2I DMs) have achieved remarkable success in generating high-quality and diverse images from text prompts, yet recent studies have revealed their vulnerability to backdoor attacks. Existing attack methods…

Cryptography and Security · Computer Science 2025-08-05 Haoran Dai , Jiawen Wang , Ruo Yang , Manali Sharma , Zhonghao Liao , Yuan Hong , Binghui Wang

Text-to-image (T2I) customization aims to create images that embody specific visual concepts delineated in textual descriptions. However, existing works still face a main challenge, concept overfitting. To tackle this challenge, we first…

Computer Vision and Pattern Recognition · Computer Science 2024-04-23 Weili Zeng , Yichao Yan , Qi Zhu , Zhuo Chen , Pengzhi Chu , Weiming Zhao , Xiaokang Yang

Text-to-image generation (T2I) refers to the text-guided generation of high-quality images. In the past few years, T2I has attracted widespread attention and numerous works have emerged. In this survey, we comprehensively review 141 works…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Pengfei Yang , Ngai-Man Cheung , Xinda Ma

Diffusion models show remarkable image generation performance following text prompts, but risk generating sexual contents. Existing approaches, such as prompt filtering, concept removal, and even sexual contents mitigation methods, struggle…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Jaesin Ahn , Heechul Jung

Artificial Intelligence-Generated Content (AIGC) has made significant strides, with high-resolution text-to-image (T2I) generation becoming increasingly critical for improving users' Quality of Experience (QoE). Although…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Chongbin Yi , Yuxin Liang , Ziqi Zhou , Peng Yang

Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, either unsafe-text paired with safe-image groundtruth or negative/positive image pairs,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Komal Kumar , Ankan Deria , Abhishek Basu , Fahad Shamshad , Hisham Cholakkal , Karthik Nandakumar

The rise of text-to-image (T2I) models has increasingly raised concerns regarding the generation of risky content, such as sexual, violent, and copyright-protected images, highlighting the need for effective safeguards within the models…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Chenyu Zhang

Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance. However, these T2I models still struggle to produce images that are aesthetically pleasing, geometrically…

Computer Vision and Pattern Recognition · Computer Science 2024-03-28 Jianshu Guo , Wenhao Chai , Jie Deng , Hsiang-Wei Huang , Tian Ye , Yichen Xu , Jiawei Zhang , Jenq-Neng Hwang , Gaoang Wang

Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the…

Computer Vision and Pattern Recognition · Computer Science 2025-10-24 Die Chen , Zhiwen Li , Cen Chen , Yuexiang Xie , Xiaodan Li , Jinyan Ye , Yingda Chen , Yaliang Li

Text-to-image (T2I) models can generate not-safe-for-work (NSFW) content, motivating multi-stage safety pipelines with both text and image filters. Newer LLM-based filters detect latent intent beyond keywords, making token-level…

Machine Learning · Computer Science 2026-05-26 Zixuan Chen , Hao Lin , Ke Xu , Xinghao Jiang , Tanfeng Sun

The success of recent text-to-image diffusion models is largely due to their capacity to be guided by a complex text prompt, which enables users to precisely describe the desired content. However, these models struggle to effectively…

Computer Vision and Pattern Recognition · Computer Science 2024-02-09 Senmao Li , Joost van de Weijer , Taihang Hu , Fahad Shahbaz Khan , Qibin Hou , Yaxing Wang , Jian Yang

Text-to-Image (T2I) diffusion models have demonstrated significant advancements in generating high-quality images, while raising potential safety concerns regarding harmful content generation. Safety-guidance-based methods have been…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Yongli Xiang , Ziming Hong , Zhaoqing Wang , Xiangyu Zhao , Bo Han , Tongliang Liu

This paper explores a novel lightweight approach LightFair to achieve fair text-to-image diffusion models (T2I DMs) by addressing the adverse effects of the text encoder. Most existing methods either couple different parts of the diffusion…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Boyu Han , Qianqian Xu , Shilong Bao , Zhiyong Yang , Kangli Zi , Qingming Huang

Advanced diffusion-based Text-to-Image (T2I) models, such as the Stable Diffusion Model, have made significant progress in generating diverse and high-quality images using text prompts alone. However, when non-famous users require…

Computer Vision and Pattern Recognition · Computer Science 2024-03-25 Yang Li , Songlin Yang , Wei Wang , Jing Dong

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. However, the effective integration of T2I models into…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Zhicai Wang , Longhui Wei , Tan Wang , Heyu Chen , Yanbin Hao , Xiang Wang , Xiangnan He , Qi Tian

Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions. The real-world applications of these models require particular attention to their safety and fidelity, but this has…

Cryptography and Security · Computer Science 2023-06-26 Hongcheng Gao , Hao Zhang , Yinpeng Dong , Zhijie Deng