Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models

2025-09-24

AI Summary

This paper systematically identifies multifaceted risks posed by diffusion models (DMs) across safety, ethics, and trust dimensions. To address these challenges, it introduces the first structured threat taxonomy and integrated defense framework unifying security, ethical, and trust-related concerns. Through a systematic literature review, multi-scenario case studies, and formal risk modeling, the work classifies salient harms—including malicious content generation, identity spoofing, bias amplification, and accountability ambiguity—and proposes corresponding mitigation strategies: enhanced technical controllability, value-aligned training mechanisms, improved model interpretability, and cross-stakeholder governance coordination. The study clarifies critical open problems in generative AI assurance and establishes a foundational theoretical and practical framework for trustworthy AI development. It provides essential support for future standardization, algorithmic auditing, and responsible innovation in generative AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Diffusion models (DMs) have been investigated in various domains due to their ability to generate high-quality data, thereby attracting significant attention. However, similar to traditional deep learning systems, there also exist potential threats to DMs. To provide advanced and comprehensive insights into safety, ethics, and trust in DMs, this survey comprehensively elucidates its framework, threats, and countermeasures. Each threat and its countermeasures are systematically examined and categorized to facilitate thorough analysis. Furthermore, we introduce specific examples of how DMs are used, what dangers they might bring, and ways to protect against these dangers. Finally, we discuss key lessons learned, highlight open challenges related to DM security, and outline prospective research directions in this critical field. This work aims to accelerate progress not only in the technical capabilities of generative artificial intelligence but also in the maturity and wisdom of its application.

Problem

• Surveying safety, ethics, and trust threats in diffusion models
• Examining countermeasures to address diffusion model vulnerabilities
• Outlining research directions for responsible generative AI development

Innovation

• Survey systematically categorizes threats and countermeasures
• Framework elucidates safety ethics trust in diffusion models
• Outlines research directions for responsible generative AI