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Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safety mechanisms and red teaming attack methods are proposed to…

Cryptography and Security · Computer Science 2025-02-07 Pucheng Dang , Xing Hu , Dong Li , Rui Zhang , Qi Guo , Kaidi Xu

AI models are being increasingly integrated into real-world systems, raising significant concerns about their safety and security. Consequently, AI red teaming has become essential for organizations to proactively identify and address…

Cryptography and Security · Computer Science 2025-10-15 Caelin Kaplan , Alexander Warnecke , Neil Archibald

With recent generative models facilitating photo-realistic image synthesis, the proliferation of synthetic images has also engendered certain negative impacts on social platforms, thereby raising an urgent imperative to develop effective…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Ouxiang Li , Jiayin Cai , Yanbin Hao , Xiaolong Jiang , Yao Hu , Fuli Feng

Understanding the capabilities of text-to-image (T2I) models in harmful content generation is essential to safety and compliance. However, human red-teaming is costly and inconsistent, driving the need for automatic tools that simulate…

Machine Learning · Computer Science 2026-05-13 Zhi-Yi Chin , Pin-Yu Chen , Wei-Chen Chiu , Mario Fritz

The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring their safe deployment. We present SIRAJ: a generic…

Cryptography and Security · Computer Science 2025-10-31 Kaiwen Zhou , Ahmed Elgohary , A S M Iftekhar , Amin Saied

Despite the integration of safety alignment and external filters, text-to-image (T2I) generative systems are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended…

Cryptography and Security · Computer Science 2025-12-09 Boheng Li , Junjie Wang , Yiming Li , Zhiyang Hu , Leyi Qi , Jianshuo Dong , Run Wang , Han Qiu , Zhan Qin , Tianwei Zhang

The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode…

Computer Vision and Pattern Recognition · Computer Science 2025-06-27 Zhenglin Huang , Jinwei Hu , Xiangtai Li , Yiwei He , Xingyu Zhao , Bei Peng , Baoyuan Wu , Xiaowei Huang , Guangliang Cheng

The advent of accessible Generative AI tools enables anyone to create and spread synthetic images on social media, often with the intention to mislead, thus posing a significant threat to online information integrity. Most existing…

Computer Vision and Pattern Recognition · Computer Science 2025-06-16 Efthymia Amarantidou , Christos Koutlis , Symeon Papadopoulos , Panagiotis C. Petrantonakis

Text-to-image (T2I) diffusion models have drawn attention for their ability to generate high-quality images with precise text alignment. However, these models can also be misused to produce inappropriate content. Existing safety measures,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Hongxiang Zhang , Yifeng He , Hao Chen

Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the recent wave of transformative AI. Nevertheless, such advance…

Computation and Language · Computer Science 2026-01-15 Zhi-Yi Chin , Chieh-Ming Jiang , Ching-Chun Huang , Pin-Yu Chen , Wei-Chen Chiu

The generative AI technology offers an increasing variety of tools for generating entirely synthetic images that are increasingly indistinguishable from real ones. Unlike methods that alter portions of an image, the creation of completely…

Computer Vision and Pattern Recognition · Computer Science 2024-04-30 Manos Schinas , Symeon Papadopoulos

While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientifically rigorous images. Existing Text-to-Image (T2I) models…

Computer Vision and Pattern Recognition · Computer Science 2026-01-27 Honglin Lin , Chonghan Qin , Zheng Liu , Qizhi Pei , Yu Li , Zhanping Zhong , Xin Gao , Yanfeng Wang , Conghui He , Lijun Wu

New advancements for the detection of synthetic images are critical for fighting disinformation, as the capabilities of generative AI models continuously evolve and can lead to hyper-realistic synthetic imagery at unprecedented scale and…

Computer Vision and Pattern Recognition · Computer Science 2023-04-25 Pantelis Dogoulis , Giorgos Kordopatis-Zilos , Ioannis Kompatsiaris , Symeon Papadopoulos

Text-to-image (T2I) models raise ethical and safety concerns due to their potential to generate inappropriate or harmful images. Evaluating these models' security through red-teaming is vital, yet white-box approaches are limited by their…

Machine Learning · Computer Science 2025-05-28 Yichuan Cao , Yibo Miao , Xiao-Shan Gao , Yinpeng Dong

With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generation of offensive images. By focusing on ``implicitly…

Text-to-image (T2I) models have become prevalent across numerous applications, making their robust evaluation against adversarial attacks a critical priority. Continuous access to new and challenging adversarial prompts across diverse…

Machine Learning · Computer Science 2025-07-25 Jessica Quaye , Charvi Rastogi , Alicia Parrish , Oana Inel , Minsuk Kahng , Lora Aroyo , Vijay Janapa Reddi

Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused. In this study, we empirically show that different timesteps of DDIM inversion reveal varying subtle…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Yixin Wu , Feiran Zhang , Tianyuan Shi , Ruicheng Yin , Zhenghua Wang , Zhenliang Gan , Xiaohua Wang , Changze Lv , Xiaoqing Zheng , Xuanjing Huang

Text-to-image (T2I) models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety…

Cryptography and Security · Computer Science 2025-06-13 Zilong Wang , Xiang Zheng , Xiaosen Wang , Bo Wang , Xingjun Ma , Yu-Gang Jiang

As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite satisfactory in-distribution performance, deep neural network-based synthetic image…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Kaixiang Zheng , Jacob H. Seidman

A red team simulates adversary attacks to help defenders find effective strategies to defend their systems in a real-world operational setting. As more enterprise systems adopt AI, red-teaming will need to evolve to address the unique…

Machine Learning · Computer Science 2025-09-16 Anusha Sinha , Keltin Grimes , James Lucassen , Michael Feffer , Nathan VanHoudnos , Zhiwei Steven Wu , Hoda Heidari
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