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Recent advances in text-to-video (T2V) diffusion models have significantly enhanced the quality of generated videos. However, their capability to produce explicit or harmful content introduces new challenges related to misuse and potential…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Xiaoyu Ye , Songjie Cheng , Yongtao Wang , Yajiao Xiong , Yishen Li

The remarkable ability of diffusion models to generate high-fidelity images has led to their widespread adoption. However, concerns have also arisen regarding their potential to produce Not Safe for Work (NSFW) content and exhibit social…

Computation and Language · Computer Science 2025-05-22 Zhiwen Li , Die Chen , Mingyuan Fan , Cen Chen , Yaliang Li , Yanhao Wang , Wenmeng Zhou

The rapid growth of text-to-image diffusion models has raised concerns about their potential misuse in generating harmful or unauthorized contents. To address these issues, several Concept Erasure methods have been proposed. However, most…

Computer Vision and Pattern Recognition · Computer Science 2025-09-09 Kien Nguyen , Anh Tran , Cuong Pham

Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images. To mitigate risk, concept erasure methods are studied to facilitate the model to…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Ruidong Chen , Honglin Guo , Lanjun Wang , Chenyu Zhang , Weizhi Nie , An-An Liu

Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including…

Machine Learning · Computer Science 2023-10-10 Minh Pham , Kelly O. Marshall , Niv Cohen , Govind Mittal , Chinmay Hegde

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

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

Recent advances in text-to-image (T2I) diffusion models have seen rapid and widespread adoption. However, their powerful generative capabilities raise concerns about potential misuse for synthesizing harmful, private, or copyrighted…

Computer Vision and Pattern Recognition · Computer Science 2026-02-24 Uichan Lee , Jeonghyeon Kim , Sangheum Hwang

Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However, this progress has raised several concerns of potential…

Machine Learning · Computer Science 2024-06-10 Yu-Lin Tsai , Chia-Yi Hsu , Chulin Xie , Chih-Hsun Lin , Jia-You Chen , Bo Li , Pin-Yu Chen , Chia-Mu Yu , Chun-Ying Huang

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 diffusion models have shown unprecedented generative capability, but their ability to produce undesirable concepts (e.g.~pornographic content, sensitive identities, copyrighted styles) poses serious concerns for privacy,…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Finn Carter

The issue of generative pretraining for vision models has persisted as a long-standing conundrum. At present, the text-to-image (T2I) diffusion model demonstrates remarkable proficiency in generating high-definition images matching textual…

Computer Vision and Pattern Recognition · Computer Science 2023-12-25 Qiang Wan , Zilong Huang , Bingyi Kang , Jiashi Feng , Li Zhang

Concept erasure in text-to-image diffusion models is crucial for mitigating harmful content, yet existing methods often compromise generative quality. We introduce Semantic Surgery, a novel training-free, zero-shot framework for concept…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Lexiang Xiong , Chengyu Liu , Jingwen Ye , Yan Liu , Yuecong Xu

Recent years have witnessed success in AIGC (AI Generated Content). People can make use of a pre-trained diffusion model to generate images of high quality or freely modify existing pictures with only prompts in nature language. More…

Cryptography and Security · Computer Science 2023-08-24 Yutong Wu , Jie Zhang , Florian Kerschbaum , Tianwei Zhang

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. However, these models are typically trained on an enormous amount of Internet data, often containing copyrighted material,…

Computer Vision and Pattern Recognition · Computer Science 2023-08-17 Nupur Kumari , Bingliang Zhang , Sheng-Yu Wang , Eli Shechtman , Richard Zhang , Jun-Yan Zhu

Concept erasure is extensively utilized in image generation to prevent text-to-image models from generating undesired content. Existing methods can effectively erase narrow concepts that are specific and concrete, such as distinct…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Yuze Cai , Jiahao Lu , Hongxiang Shi , Yichao Zhou , Hong Lu

Recent advances in text-to-image diffusion models have enabled the photorealistic generation of images from text prompts. Despite the great progress, existing models still struggle to generate compositional multi-concept images naturally,…

Computer Vision and Pattern Recognition · Computer Science 2023-10-12 Hazarapet Tunanyan , Dejia Xu , Shant Navasardyan , Zhangyang Wang , Humphrey Shi

Text-to-image diffusion models have shown an impressive ability to generate high-quality images from input textual descriptions. However, concerns have been raised about the potential for these models to create content that infringes on…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Tingxu Han , Weisong Sun , Yanrong Hu , Chunrong Fang , Yonglong Zhang , Shiqing Ma , Tao Zheng , Zhenyu Chen , Zhenting Wang

Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learned by these models remain an enigma. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2023-10-06 Hila Chefer , Oran Lang , Mor Geva , Volodymyr Polosukhin , Assaf Shocher , Michal Irani , Inbar Mosseri , Lior Wolf

Removing undesired concepts from large-scale text-to-image (T2I) and text-to-video (T2V) diffusion models while preserving overall generative quality remains a major challenge, particularly as modern models such as Stable Diffusion v3,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Zhaoxin Fan , Nanxiang Jiang , Daiheng Gao , Shiji Zhou , Wenjun Wu