实现针对恶意图像编辑的可转移防御.
概括
我们开发了一种新的防御框架,即针对恶意图像编辑的可转移防御 (TDAE),以保护图像免受有害的编辑. 通过在编辑过程中优化图像和文本,TDAE提高了跨模型的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 扩散模型能够进行先进的图像编辑,但容易受到恶意操纵的影响.
- 现有的防御在不同的编辑模型中缺乏可转移性.
- 不能察觉到的扰动显示出潜力,但面临限制.
研究的目的:
- 提出一种新的框架,即针对恶意图像编辑的可转移防御 (TDAE),用于针对恶意图像编辑的强有力的防御.
- 增强图像对未见的编辑模型的免疫力,并实现跨模型的可转移性.
- 提高基于扩散的图像编辑系统的安全性.
主要方法:
- 引入了一个双式框架,将视觉和文字防御策略结合起来.
- 开发了FlatGrad防御机制 (FDM) 用于梯度规范化,以促进平面最小值的稳定性.
- 建议使用对抗性优化用于跨模型对齐的文本嵌入的动态提示防御 (DPD).
主要成果:
- 在缓解恶意编辑方面,TDAE展示了最先进的性能.
- 在模型内部和跨模型评估中实现了显著的稳定性改进.
- FDM和DPD机制有效地增强了图像免疫性和跨模型可转移性.
结论:
- 拟议的TDAE框架在捍卫基于扩散的图像编辑系统方面取得了重大进展.
- 协调图像-文本优化对于实现对恶意编辑的可转移防御至关重要.
- TDAE提供了一个强大的解决方案,用于增强生成AI应用中的图像安全性.
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