在微调模型中使用恒定重量代码对卷积层的白框水印
Minoru Kuribayashi1, Tatsuya Yasui1, Asad Malik2
1Graduate School of Natural Science and Technology, Okayama University, Okayama 700-8530, Japan.
Journal of imaging
|June 27, 2023
概括
这项研究通过在任何卷积层中嵌入水标来增强深度神经网络 (DNN) 水标,而不仅仅是完全连接的水标. 非可真菌代币确保了DNN知识产权保护的水标完整性和创建时间验证.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 深度神经网络 (DNN) 水印保护知识产权,但面临诸如神经元修剪和有限的嵌入层等挑战.
- 现有的方法往往侧重于对再培训和微调的强度,水印通常只嵌入完全连接的层.
研究的目的:
- 扩展DNN水印技术,适用于DNN模型中的任何卷积层.
- 利用提取的重量参数进行统计分析,开发出强大的水印探测器.
- 利用非真菌代币来增强水印安全性和时间印.
主要方法:
- 开发了一种扩展的DNN水印方法,适用于卷积层.
- 基于对重量参数的统计分析设计了一个水印探测器.
- 集成的非真菌代币技术,以防止水印覆盖和记录创建时间.
主要成果:
- 拟议的方法成功地将水印嵌入到卷积层中,扩大了适用于完全连接的层之外的应用范围.
- 统计水印检测器有效地识别了水印的存在.
- 非真菌代币提供了一种安全和可验证的方法来保护DNN水印.
结论:
- 这项研究通过使水印嵌入到任何卷积层来显著推进DNN水印.
- 统计分析和非真菌代币的整合为保护DNN模型提供了更强大,更安全的解决方案.
- 这些发现有助于保障人工智能领域的知识产权.
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