用纠错码技术实现文本到图像模型的精准指纹标记，解决部署不可靠问题。
PALADIN : Robust Neural Fingerprinting for Text-to-Image Diffusion Models
- 引入循环纠错码思想，设计可精准溯源的神经指纹
- 在扩散模型中实现100%指纹识别准确率，突破现有方法瓶颈
- 适合需高可靠性模型溯源的安全部门与合规场景
文本到图像生成模型因开源发展面临被滥用的风险，模型溯源成为关键防护手段。当前神经指纹技术虽广泛研究，但普遍存在溯源准确率与生成质量的权衡，且尚未有方法达到100%准确率，导致实际部署不可行。本文提出PALADIN，基于编码理论中的循环纠错码，为文本到图像扩散模型设计高精度神经指纹方案，在保持生成质量的同时实现100%溯源准确率，显著提升模型可追踪性与安全性。
原文摘要 · Abstract (English)
The risk of misusing text-to-image generative models for malicious uses, especially due to the open-source development of such models, has become a serious concern. As a risk mitigation strategy, attributing generative models with neural fingerprinting is emerging as a popular technique. There has been a plethora of recent work that aim for addressing neural fingerprinting. A trade-off between the attribution accuracy and generation quality of such models has been studied extensively. None of the existing methods yet achieved 100% attribution accuracy. However, any model with less than cent percent accuracy is practically non-deployable. In this work, we propose an accurate method to incorporate neural fingerprinting for text-to-image diffusion models leveraging the concepts of cyclic error correcting codes from the literature of coding theory.
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