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RWP:一个强大的水标插件,用于在稳定扩散模型中进行归因和保护
Zongxin Liu1, Jinhong Zhang2, Yunyun Dong2
1National Pilot School of Software, Yunnan University, Kunming, 650504, Yunnan, China; School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China.
概括
强大的水印插件RWP通过将可追踪信息直接嵌入扩散模型来增强AI内容安全性. 这提高了用户追踪的准确性和容量,为商业应用提供了强大的版权保护.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 信息安全 信息安全
背景情况:
- 扩散模型在内容生成方面表现出色,但在追踪来源方面存在挑战.
- 滥用黑盒模型和欺骗性内容需要强有力的解决方案.
- 现有的水印方法与商业人工智能场景和用户追踪精度作斗争.
研究的目的:
- 介绍RWP,一个强大的水标插件用于AI商业应用.
- 解决扩散模型当前水印技术的局限性.
- 提高人工智能产生的内容的可追溯性和问责制.
主要方法:
- 在扩散模型的生成过程中,通过可逆加密模块嵌入可追溯的水印信息.
- 开发一种基于插件的方法,不需要对扩散模型架构进行任何修改.
- 整合独特的转换序列,以实现强大的数据嵌入.
主要成果:
- 与AquaLoRA相比,在与图像对图像翻译和inpainting攻击相比,在跟踪精度上实现20-50%的改进.
- 保持生成内容的高视觉真实性.
- 将可追溯用户容量从48-64位扩展到400位以上,以实现更细致的识别.
结论:
- 在大型商业人工智能应用中,RWP为版权保护和问责制提供了实用和有效的解决方案.
- 插件架构显著提高了水标系统的部署效率.
- 增强的用户容量支持可靠的追踪,并阻止模型的滥用.
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