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

• First comprehensive survey dedicated to the non-functional properties of text-to-image diffusion models (T2I DMs). • A concise taxonomy covering non-functional properties, study means, benchmarks, and applications. • Systematic analysis of 96 papers, including definitions, metrics, and methodological comparisons. • Summary of benchmarks and real-world applications relevant to trustworthy T2I DMs. • Identification of key research gaps and future directions, with an accompanying up-to-date GitHub repository. 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 .