系统梳理扩散模型的安全与伦理风险及应对策略
Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models
- 从框架、威胁到对策分层解析扩散模型风险
- 揭示生成内容可能带来的伦理与安全问题
- 适合关注AI伦理与可信生成的研究者阅读
扩散模型（DMs）因其生成高质量数据的能力，在多个领域受到广泛关注。然而，与传统深度学习系统类似，扩散模型也存在潜在威胁。本文系统梳理了扩散模型在安全、伦理与信任方面的框架、威胁及应对措施，对各类威胁及其缓解方法进行分类分析。通过具体案例说明扩散模型的应用场景、潜在风险及防护手段。最后总结关键经验，指出当前安全挑战，并展望未来研究方向。本工作旨在推动生成式人工智能技术能力提升的同时，促进其应用的成熟与审慎。
原文摘要 · Abstract (English)
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.
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