Trustworthy text-to-image diffusion models : A timely and focused survey

Zhang, Yi and Chen, Zhen and Cheng, Chih-Hong and Ruan, Wenjie and Huang, Xiaowei and Zhao, Dezong and Flynn, David and Khastgir, Siddartha and Zhao, Xingyu (2026) Trustworthy text-to-image diffusion models : A timely and focused survey. Information Fusion, 133: 104264. ISSN 1566-2535

Abstract

Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of trustworthiness, such as robustness, fairness, security, privacy, and explainability, similar to those in traditional deep learning (DL) tasks. Conventional approaches for studying trustworthiness in DL tasks often inadequate for T2I DMs because of their unique characteristics, e.g., multi-modal nature, stochastic generation process, and high computational cost. Given these challenge, recent efforts have been made to develop new methods for investigating trustworthiness in T2I DMs via various means, including falsification, enhancement, verification and assessment. However, there is a notable lack of in-depth analysis concerning those non-functional properties and means. In this survey, we provide a timely and focused review of the literature on trustworthy T2I DMs, covering a concise-structured taxonomy from the perspectives of property, means, benchmarks and applications. Our review begins with an introduction to essential preliminaries of T2I DMs, and then we summarize key definitions and metrics specific to T2I tasks, based on which we analyze the corresponding means in recent literature. Additionally, we review benchmarks and domain applications of T2I DMs. Finally, we highlight the gaps in current research, discuss the limitations of existing methods, and propose future research directions to advance the development of trustworthy T2I DMs. Furthermore, we keep up-to-date updates in this field to track the latest developments and maintain our GitHub repository at: https://github.com/wellzline/Trustworthy_T2I_DMs