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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

The rapid advancement of text-to-image (T2I) models, such as Stable Diffusion, has enhanced their capability to synthesize images from textual prompts. However, this progress also raises significant risks of misuse, including the generation…

Computer Vision and Pattern Recognition · Computer Science 2025-06-24 Yu Xie , Chengjie Zeng , Lingyun Zhang , Yanwei Fu

Advanced text-to-image models such as DALL$\cdot$E 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential proliferation of unsafe content. This includes adult,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Zhongjie Ba , Jieming Zhong , Jiachen Lei , Peng Cheng , Qinglong Wang , Zhan Qin , Zhibo Wang , Kui Ren

Text-to-image (T2I) models can be maliciously used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. Previous attacks largely depend on the availability of the diffusion…

Cryptography and Security · Computer Science 2025-05-27 Jiachen Ma , Yijiang Li , Zhiqing Xiao , Anda Cao , Jie Zhang , Chao Ye , Junbo Zhao

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

State-of-the-art Diffusion Models (DMs) produce highly realistic images. While prior work has successfully mitigated Not Safe For Work (NSFW) content in the visual domain, we identify a novel threat: the generation of NSFW text embedded…

Computer Vision and Pattern Recognition · Computer Science 2026-01-16 Aditya Kumar , Tom Blanchard , Adam Dziedzic , Franziska Boenisch

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

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) 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

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\cdot$2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate…

Computer Vision and Pattern Recognition · Computer Science 2023-08-17 Yiting Qu , Xinyue Shen , Xinlei He , Michael Backes , Savvas Zannettou , Yang Zhang

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 (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

Diffusion models have recently achieved remarkable advancements in terms of image quality and fidelity to textual prompts. Concurrently, the safety of such generative models has become an area of growing concern. This work introduces a…

Computer Vision and Pattern Recognition · Computer Science 2025-06-12 Tong Liu , Zhixin Lai , Jiawen Wang , Gengyuan Zhang , Shuo Chen , Philip Torr , Vera Demberg , Volker Tresp , Jindong Gu

Text-to-image generative models are widely deployed in creative tools and online platforms. To mitigate misuse, these systems rely on safety filters and moderation pipelines that aim to block harmful or policy violating content. In this…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Ahmed B Mustafa , Zihan Ye , Yang Lu , Michael P Pound , Shreyank N Gowda

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

Text-to-Image models may generate harmful content, such as pornographic images, particularly when unsafe prompts are submitted. To address this issue, safety filters are often added on top of text-to-image models, or the models themselves…

Cryptography and Security · Computer Science 2026-01-09 Zhengyuan Jiang , Yuepeng Hu , Yuchen Yang , Yinzhi Cao , Neil Zhenqiang Gong

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

Malicious or manipulated prompts are known to exploit text-to-image models to generate unsafe images. Existing studies, however, focus on the passive exploitation of such harmful capabilities. In this paper, we investigate the proactive…

Cryptography and Security · Computer Science 2025-02-06 Yixin Wu , Ning Yu , Michael Backes , Yun Shen , Yang Zhang

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

With advances in diffusion models, image generation has shown significant performance improvements. This raises concerns about the potential abuse of image generation, such as the creation of explicit or violent images, commonly referred to…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Junha Park , Jaehui Hwang , Ian Ryu , Hyungkeun Park , Jiyoon Kim , Jong-Seok Lee
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