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Recent advancements in Text-to-Image (T2I) models have raised significant safety concerns about their potential misuse for generating inappropriate or Not-Safe-For-Work (NSFW) contents, despite existing countermeasures such as NSFW…

Computer Vision and Pattern Recognition · Computer Science 2024-10-31 Yijun Yang , Ruiyuan Gao , Xiao Yang , Jianyuan Zhong , Qiang Xu

Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation of Not Safe for Work (NSFW) content. Previous research has…

Computer Vision and Pattern Recognition · Computer Science 2025-09-15 Yaopei Zeng , Yuanpu Cao , Bochuan Cao , Yurui Chang , Jinghui Chen , Lu Lin

In the past years, we have witnessed the remarkable success of Text-to-Image (T2I) models and their widespread use on the web. Extensive research in making T2I models produce hyper-realistic images has led to new concerns, such as…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Muhammad Shahid Muneer , Simon S. Woo

Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can generate offensive or disturbing imagery when provided with harmful inputs. Existing…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Chi Zhang , Changjia Zhu , Xiaowen Li , Yao Liu , Zhuo Lu

Text-to-image (T2I) models have significantly advanced in producing high-quality images. However, such models have the ability to generate images containing not-safe-for-work (NSFW) content, such as pornography, violence, political content,…

Cryptography and Security · Computer Science 2025-05-15 Longtian Wang , Xiaofei Xie , Tianlin Li , Yuhan Zhi , Chao Shen

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

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-based text-to-image (T2I) models enable high-quality image generation but also pose significant risks of misuse, particularly in producing not-safe-for-work (NSFW) content. While prior detection methods have focused on filtering…

Computer Vision and Pattern Recognition · Computer Science 2025-08-06 Fan Yang , Yihao Huang , Jiayi Zhu , Ling Shi , Geguang Pu , Jin Song Dong , Kailong Wang

Text-to-image (T2I) models, such as Stable Diffusion, have exhibited remarkable performance in generating high-quality images from text descriptions in recent years. However, text-to-image models may be tricked into generating…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Xinfeng Li , Yuchen Yang , Jiangyi Deng , Chen Yan , Yanjiao Chen , Xiaoyu Ji , Wenyuan Xu

Using risky text prompts, such as pornography and violent prompts, to test the safety of text-to-image (T2I) models is a critical task. However, existing risky prompt datasets are limited in three key areas: 1) limited risky categories, 2)…

Cryptography and Security · Computer Science 2025-11-24 Chenyu Zhang , Tairen Zhang , Lanjun Wang , Ruidong Chen , Wenhui Li , Anan Liu

Recent advancements in text-to-image (T2I) models have unlocked a wide range of applications but also present significant risks, particularly in their potential to generate unsafe content. To mitigate this issue, researchers have developed…

Computer Vision and Pattern Recognition · Computer Science 2025-01-17 Yong-Hyun Park , Sangdoo Yun , Jin-Hwa Kim , Junho Kim , Geonhui Jang , Yonghyun Jeong , Junghyo Jo , Gayoung Lee

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 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

Recent text-to-image (T2I) models have had great success, and many benchmarks have been proposed to evaluate their performance and safety. However, they only consider explicit prompts while neglecting implicit prompts (hint at a target…

Computers and Society · Computer Science 2024-05-29 Yue Yang , Yuqi Lin , Hong Liu , Wenqi Shao , Runjian Chen , Hailong Shang , Yu Wang , Yu Qiao , Kaipeng Zhang , Ping Luo

With the advance of generative AI, the text-to-image (T2I) model has the ability to generate various contents. However, the generated contents cannot be fully controlled. There is a potential risk that T2I model can generate unsafe images…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Dong Han , Salaheldin Mohamed , Yong Li

Text-to-Image (T2I) diffusion models are widely recognized for their ability to generate high-quality and diverse images based on text prompts. However, despite recent advances, these models are still prone to generating unsafe images…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Jiangweizhi Peng , Zhiwei Tang , Gaowen Liu , Charles Fleming , Mingyi Hong

Various (text) prompt filters and (image) safety checkers have been implemented to mitigate the misuse of Text-to-Image (T2I) models in creating Not-Safe-For-Work (NSFW) content. In order to expose potential security vulnerabilities of such…

Cryptography and Security · Computer Science 2025-08-12 Song Yan , Hui Wei , Jinlong Fei , Guoliang Yang , Zhengyu Zhao , Zheng Wang

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

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

Not Safe/Suitable for Work (NSFW) content is rampant on social networks and poses serious harm to citizens, especially minors. Current detection methods mainly rely on deep learning-based image recognition and classification. However, NSFW…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Han Bao , Qinying Wang , Zhi Chen , Qingming Li , Xuhong Zhang , Changjiang Li , Zonghui Wang , Shouling Ji , Wenzhi Chen
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