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Related papers: Harm Amplification in Text-to-Image Models

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The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense…

Human-Computer Interaction · Computer Science 2024-03-08 Zhijie Wang , Yuheng Huang , Da Song , Lei Ma , Tianyi Zhang

Human motion generation driven by deep generative models has enabled compelling applications, but the ability of text-to-motion (T2M) models to produce realistic motions from text prompts raises security concerns if exploited maliciously.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-02 Honglei Miao , Fan Ma , Ruijie Quan , Kun Zhan , Yi Yang

Modern text-to-image (T2I) models can now render legible, paragraph-length text, enabling a fundamentally new class of misuse. We identify and formalize the inscriptive jailbreak, where an adversary coerces a T2I system into generating…

Computer Vision and Pattern Recognition · Computer Science 2026-04-09 Zonghao Ying , Haowen Dai , Lianyu Hu , Zonglei Jing , Quanchen Zou , Yaodong Yang , Aishan Liu , Xianglong Liu

Text-to-image generative models often reflect the biases of the training data, leading to unequal representations of underrepresented groups. This study investigates inclusive text-to-image generative models that generate images based on…

Computer Vision and Pattern Recognition · Computer Science 2023-09-12 Cheng Zhang , Xuanbai Chen , Siqi Chai , Chen Henry Wu , Dmitry Lagun , Thabo Beeler , Fernando De la Torre

Text-to-image models have shown remarkable progress in generating high-quality images from user-provided prompts. Despite this, the quality of these images varies due to the models' sensitivity to human language nuances. With advancements…

Artificial Intelligence · Computer Science 2024-06-14 Xinrui Yang , Zhuohan Wang , Anthony Hu

Current Cross-Modality Generation Models (GMs) demonstrate remarkable capabilities in various generative tasks. Given the ubiquity and information richness of vision modality inputs in real-world scenarios, Cross-Vision tasks, encompassing…

Computer Vision and Pattern Recognition · Computer Science 2025-11-06 Hao Cheng , Erjia Xiao , Yichi Wang , Lingfeng Zhang , Qiang Zhang , Jiahang Cao , Kaidi Xu , Mengshu Sun , Xiaoshuai Hao , Jindong Gu , Renjing Xu

In recent years, fueled by the rapid advancement of diffusion models, text-to-video (T2V) generation models have achieved remarkable progress, with notable examples including Pika, Luma, Kling, and Open-Sora. Although these models exhibit…

Computer Vision and Pattern Recognition · Computer Science 2025-06-18 Jiayang Liu , Siyuan Liang , Shiqian Zhao , Rongcheng Tu , Wenbo Zhou , Aishan Liu , Dacheng Tao , Siew Kei Lam

Visual Generative AI models have demonstrated remarkable capability in generating high-quality images from user inputs like text prompts. However, because these models have billions of parameters, they risk memorizing certain parts of the…

Computer Vision and Pattern Recognition · Computer Science 2025-12-18 Lena Reissinger , Yuanyuan Li , Anna-Carolina Haensch , Neeraj Sarna

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-to-Image generation models have revolutionized the artwork design process and enabled anyone to create high-quality images by entering text descriptions called prompts. Creating a high-quality prompt that consists of a subject and…

Cryptography and Security · Computer Science 2024-04-16 Xinyue Shen , Yiting Qu , Michael Backes , Yang Zhang

The widespread use of Text-to-Image (T2I) models in content generation requires careful examination of their safety, including their robustness to adversarial attacks. Despite extensive research on adversarial attacks, the reasons for their…

Machine Learning · Computer Science 2024-07-18 Haz Sameen Shahgir , Xianghao Kong , Greg Ver Steeg , Yue Dong

Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with implicit sexual prompts. Unlike explicit harmful prompts,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-20 Yitong Sun , Yao Huang , Ruochen Zhang , Huanran Chen , Shouwei Ruan , Ranjie Duan , Xingxing Wei

In light of the mounting imperative for public security, the necessity for automated threat detection in high-risk scenarios is becoming increasingly pressing. However, existing methods generally suffer from the problems of uninterpretable…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Yuhan Wang , Cheng Liu , Daou Zhang , Zihan Zhao , Jinyang Chen , Purui Dong , Zuyuan Yu , Ziru Wang , Weichao Wu

Text-to-image generative models have recently garnered significant attention due to their ability to generate images based on prompt descriptions. While these models have shown promising performance, concerns have been raised regarding the…

Computer Vision and Pattern Recognition · Computer Science 2025-09-09 Meiling Li , Zhenxing Qian , Xinpeng Zhang

The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent, often requiring `prompt engineering'. To harness visual concepts from target images without prompt…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Shweta Mahajan , Tanzila Rahman , Kwang Moo Yi , Leonid Sigal

Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts. Among various approaches, diffusion models have achieved…

Computer Vision and Pattern Recognition · Computer Science 2026-01-26 Xuyang Guo , Jiayan Huo , Yingyu Liang , Zhenmei Shi , Zhao Song , Jiahao Zhang , Zhen Zhuang

Text-to-image (T2I) models, though exhibiting remarkable creativity in image generation, can be exploited to produce unsafe images. Existing safety measures, e.g., content moderation or model alignment, fail in the presence of white-box…

Cryptography and Security · Computer Science 2025-10-21 Xinfeng Li , Shengyuan Pang , Jialin Wu , Jiangyi Deng , Huanlong Zhong , Yanjiao Chen , Jie Zhang , Wenyuan Xu

Ethical issues around text-to-image (T2I) models demand a comprehensive control over the generative content. Existing techniques addressing these issues for responsible T2I models aim for the generated content to be fair and safe…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Basim Azam , Naveed Akhtar

Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved tremendous success. Yet, aligning these models with user's intentions still involves a laborious trial-and-error process, and this challenging alignment…

Machine Learning · Computer Science 2025-02-12 Chao Wang , Giulio Franzese , Alessandro Finamore , Massimo Gallo , Pietro Michiardi

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. However, the effective integration of T2I models into…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Zhicai Wang , Longhui Wei , Tan Wang , Heyu Chen , Yanbin Hao , Xiang Wang , Xiangnan He , Qi Tian
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