隐形水标框架用于在线服务中未学习的传播模型.
Linlin Wang1, Tianqing Zhu1, Longxiang Gao2
1Faculty of Data Science, city University of Macau, Macao, China.
概括
本研究介绍了扩散模型的先进隐形水印技术,确保在机器取消学习期间的版权保护和内容完整性. 该方法保持了水印的有效性,而不会降低图像质量或忘记功能.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 数字法医学数字法医学
背景情况:
- 扩散模型在图像生成方面表现出色,但也引发了版权和伦理方面的担忧.
- 机器取消学习和水印对于解决这些问题至关重要.
- 现有的方法面临着取消学习和水印之间的冲突.
研究的目的:
- 为未学习的扩散模型提出先进的隐形水印技术.
- 确保版权保护和道德内容的生成.
- 解决机器取消学习和水印之间的冲突.
主要方法:
- 开发了一种先进的隐形水印技术,与未经学习的扩散模型集成.
- 为嵌入水标设计了一个优化的噪声生成过程.
- 改进了用于自适应嵌入的水印生成器.
主要成果:
- 成功嵌入水印,而不会影响取消学习的操作.
- 保持水印的隐形性,强度和可检测性.
- 确保生成的内容质量不会因忘记学习而降低.
结论:
- 提出的技术有效地平衡了在传播模型中的版权保护和内容删除.
- 这种方法可以在未经学习的模型中实现强大的和自适应的水印.
- 它解决了在生成模型中道德AI和知识产权的挑战.
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