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Text-to-Image (T2I) generation is a popular AI-generated content (AIGC) technology enabling diverse and creative image synthesis. However, some outputs may contain Not Safe For Work (NSFW) content (e.g., violence), violating community…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Mingrui Liu , Sixiao Zhang , Cheng Long

Diffusion models excel in many generative modeling tasks, notably in creating images from text prompts, a task referred to as text-to-image (T2I) generation. Despite the ability to generate high-quality images, these models often replicate…

Multimedia · Computer Science 2024-02-20 Yang Zhang , Teoh Tze Tzun , Lim Wei Hern , Haonan Wang , Kenji Kawaguchi

Text-to-image (T2I) diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of these models can inadvertently led they to…

Computer Vision and Pattern Recognition · Computer Science 2025-02-19 Die Chen , Zhiwen Li , Cen Chen , Xiaodan Li , Jinyan Ye

Text-to-image (T2I) models have demonstrated remarkable generative capabilities but remain vulnerable to producing not-safe-for-work (NSFW) content, such as violent or explicit imagery. While recent moderation efforts have introduced soft…

Computer Vision and Pattern Recognition · Computer Science 2025-08-15 Zonglei Jing , Xiao Yang , Xiaoqian Li , Siyuan Liang , Aishan Liu , Mingchuan Zhang , Xianglong Liu

Text-to-Image (T2I) diffusion models have demonstrated strong generation ability, but their potential to generate unsafe content raises significant safety concerns. Existing inference-time defense methods typically perform category-agnostic…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Binhong Tan , Zhaoxin Wang , Handing Wang

As text-to-image (T2I) models advance and gain widespread adoption, their associated safety concerns are becoming increasingly critical. Malicious users exploit these models to generate Not-Safe-for-Work (NSFW) images using harmful or…

Cryptography and Security · Computer Science 2025-12-10 Yiming Wang , Jiahao Chen , Qingming Li , Tong Zhang , Rui Zeng , Xing Yang , Shouling Ji

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

Recently, the strong latent Diffusion Probabilistic Model (DPM) has been applied to high-quality Text-to-Image (T2I) generation (e.g., Stable Diffusion), by injecting the encoded target text prompt into the gradually denoised diffusion…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Mingyang Yi , Aoxue Li , Yi Xin , Zhenguo Li

Recent advancements in Text-to-Image (T2I) diffusion models have demonstrated impressive success in generating high-quality images with zero-shot generalization capabilities. Yet, current models struggle to closely adhere to prompt…

Computer Vision and Pattern Recognition · Computer Science 2024-01-31 Hyun Kang , Dohae Lee , Myungjin Shin , In-Kwon Lee

Text-guided image manipulation with diffusion models enables flexible and precise editing based on prompts, but raises ethical and copyright concerns due to potential unauthorized modifications. To address this, we propose SecureT2I, a…

Cryptography and Security · Computer Science 2025-07-08 Xiaodong Wu , Xiangman Li , Qi Li , Jianbing Ni , Rongxing Lu

Text-to-image generative models such as Stable Diffusion and DALL$\cdot$E raise many ethical concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones. To address these ethical concerns, safety filters are often…

Machine Learning · Computer Science 2023-11-14 Yuchen Yang , Bo Hui , Haolin Yuan , Neil Gong , Yinzhi Cao

Text-to-image (T2I) models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety…

Cryptography and Security · Computer Science 2025-06-13 Zilong Wang , Xiang Zheng , Xiaosen Wang , Bo Wang , Xingjun Ma , Yu-Gang Jiang

Text-to-Image (T2I) diffusion models have rapidly advanced, enabling the generation of high-quality images that align closely with textual descriptions. However, this progress has also raised concerns about their misuse for propaganda and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-25 Jaechul Roh , Andrew Yuan , Jinsong Mao

Text-to-image models are increasingly popular and impactful, yet concerns regarding their safety and fairness remain. This study investigates the ability of ten popular Stable Diffusion models to generate harmful images, including NSFW,…

Computers and Society · Computer Science 2025-08-29 Matthias Schneider , Thilo Hagendorff

Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, these models are vulnerable to misuse, particularly generating not-safe-for-work (NSFW) content, such…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Lingzhi Yuan , Xinfeng Li , Chejian Xu , Guanhong Tao , Xiaojun Jia , Yihao Huang , Wei Dong , Yang Liu , Bo Li

Large-scale text-to-image (T2I) diffusion models have achieved remarkable generative performance about various concepts. With the limitation of privacy and safety in practice, the generative capability concerning NSFW (Not Safe For Work)…

Computer Vision and Pattern Recognition · Computer Science 2025-10-01 Jiahang Tu , Qian Feng , Jiahua Dong , Hanbin Zhao , Chao Zhang , Nicu Sebe , Hui Qian

Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusion models gradually synthesize images in an iterative fashion…

Computer Vision and Pattern Recognition · Computer Science 2023-03-15 Yogesh Balaji , Seungjun Nah , Xun Huang , Arash Vahdat , Jiaming Song , Qinsheng Zhang , Karsten Kreis , Miika Aittala , Timo Aila , Samuli Laine , Bryan Catanzaro , Tero Karras , Ming-Yu Liu

Despite their impressive capabilities, diffusion-based text-to-image (T2I) models can lack faithfulness to the text prompt, where generated images may not contain all the mentioned objects, attributes or relations. To alleviate these…

Computer Vision and Pattern Recognition · Computer Science 2023-05-23 Shyamgopal Karthik , Karsten Roth , Massimiliano Mancini , Zeynep Akata

Text-to-image diffusion models have been widely adopted in real-world applications due to their ability to generate realistic images from textual descriptions. However, recent studies have shown that these methods are vulnerable to backdoor…

Computer Vision and Pattern Recognition · Computer Science 2024-08-29 Oscar Chew , Po-Yi Lu , Jayden Lin , Hsuan-Tien Lin

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