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Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the potential generation of inappropriate content, such as biased or…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Hang Li , Chengzhi Shen , Philip Torr , Volker Tresp , Jindong Gu

Recent advances in text-to-image (T2I) diffusion models have enabled impressive generative capabilities, but they also raise significant safety concerns due to the potential to produce harmful or undesirable content. While concept erasure…

Computer Vision and Pattern Recognition · Computer Science 2026-04-09 Nanxiang Jiang , Zhaoxin Fan , Enhan Kang , Daiheng Gao , Yun Zhou , Yanxia Chang , Zheng Zhu , Yeying Jin , Wenjun Wu

Do the rich representations of multi-modal diffusion transformers (DiTs) exhibit unique properties that enhance their interpretability? We introduce ConceptAttention, a novel method that leverages the expressive power of DiT attention…

Computer Vision and Pattern Recognition · Computer Science 2025-07-03 Alec Helbling , Tuna Han Salih Meral , Ben Hoover , Pinar Yanardag , Duen Horng Chau

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

Owing to the unrestricted nature of the content in the training data, large text-to-image diffusion models, such as Stable Diffusion (SD), are capable of generating images with potentially copyrighted or dangerous content based on…

Computer Vision and Pattern Recognition · Computer Science 2023-08-09 Zixuan Ni , Longhui Wei , Jiacheng Li , Siliang Tang , Yueting Zhuang , Qi Tian

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 (T2I) models have made remarkable progress in generating high-quality, diverse visual content from natural language prompts. However, their ability to reproduce copyrighted styles, sensitive imagery, and harmful content raises…

Computer Vision and Pattern Recognition · Computer Science 2025-06-09 Changhoon Kim , Yanjun Qi

With the development of diffusion-based customization methods like DreamBooth, individuals now have access to train the models that can generate their personalized images. Despite the convenience, malicious users have misused these…

Computer Vision and Pattern Recognition · Computer Science 2024-07-29 Yisu Liu , Jinyang An , Wanqian Zhang , Dayan Wu , Jingzi Gu , Zheng Lin , Weiping Wang

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

Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-based methods for safe…

Computer Vision and Pattern Recognition · Computer Science 2025-03-17 Jaehong Yoon , Shoubin Yu , Vaidehi Patil , Huaxiu Yao , Mohit Bansal

Recent advancements in Virtual Try-On (VTO) have demonstrated exceptional efficacy in generating realistic images and preserving garment details, largely attributed to the robust generative capabilities of text-to-image (T2I) diffusion…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Zhenchen Wan , Yanwu Xu , Zhaoqing Wang , Feng Liu , Tongliang Liu , Mingming Gong

High-fidelity video generation remains challenging for diffusion models due to the difficulty of modeling complex spatio-temporal dynamics efficiently. Recent video diffusion methods typically represent a video as a sequence of…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Minh Khoa Le , Kien Do , Duc Thanh Nguyen , Truyen Tran

The widespread deployment of large vision models such as Stable Diffusion raises significant legal and ethical concerns, as these models can memorize and reproduce copyrighted content without authorization. Existing detection approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-12-29 Xiafeng Man , Zhipeng Wei , Jingjing Chen

This paper addresses the societal concerns arising from large-scale text-to-image diffusion models for generating potentially harmful or copyrighted content. Existing models rely heavily on internet-crawled data, wherein problematic…

Computer Vision and Pattern Recognition · Computer Science 2024-08-01 Sanghyun Kim , Seohyeon Jung , Balhae Kim , Moonseok Choi , Jinwoo Shin , Juho Lee

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions, enabling vast applications in content generation. While recent advancements have introduced…

Computer Vision and Pattern Recognition · Computer Science 2024-02-28 Jiun Tian Hoe , Xudong Jiang , Chee Seng Chan , Yap-Peng Tan , Weipeng Hu

The commercialization of text-to-image diffusion models (DMs) brings forth potential copyright concerns. Despite numerous attempts to protect DMs from copyright issues, the vulnerabilities of these solutions are underexplored. In this…

Cryptography and Security · Computer Science 2024-05-28 Haonan Wang , Qianli Shen , Yao Tong , Yang Zhang , Kenji Kawaguchi

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

Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey appropriate spatial composition specified by a layout…

Computer Vision and Pattern Recognition · Computer Science 2023-11-28 Jiayu Xiao , Henglei Lv , Liang Li , Shuhui Wang , Qingming Huang

The proliferation of text-to-image diffusion models (T2I DMs) has led to an increased presence of AI-generated images in daily life. However, biased T2I models can generate content with specific tendencies, potentially influencing people's…

Computer Vision and Pattern Recognition · Computer Science 2025-04-03 Huayang Huang , Xiangye Jin , Jiaxu Miao , Yu Wu

Recent text-to-image (T2I) diffusion models show outstanding performance in generating high-quality images conditioned on textual prompts. However, they fail to semantically align the generated images with the prompts due to their limited…

Computer Vision and Pattern Recognition · Computer Science 2023-12-15 Ruichen Wang , Zekang Chen , Chen Chen , Jian Ma , Haonan Lu , Xiaodong Lin