通过模型算术运算实现无损的石法图形网络
相关实验视频
Updated: Sep 13, 2025

04:48
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
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Yao Fan1, Fuqiang Di1, Mingqing Zhang1
1Engineering University of PAP, Xi'an, 710086, Shaanxi, China.
概括
这项研究介绍了一种使用模型算术运算的新型无损稳定图网络方法. 这种技术安全地嵌入深度神经网络 (DNN),用于在不影响性能的情况下进行秘密传输.
科学领域:
- 计算机科学 计算机科学
- 密码学 密码学 密码学 密码学
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 用于隐形摄影,需要安全传输.
- 现有的秘密传输DNN的方法可能会降低秘密模型的性能.
- 确保隐藏模型在隐藏记录过程中的完整性对于它们的功能至关重要.
研究的目的:
- 提出一种新的方法,用于DNN的无损稳定传输.
- 确保在盗记录过程中秘密模型的完全转移和完整性.
- 为了实现DNN的安全和秘密通信,而不会降低性能.
主要方法:
- 使用模型算术运算开发了一个无损的石图网络.
- 在使用算术运算的stego模型中隐藏秘密模型参数.
- 采用了一种代训练方法来优化stego模型,以获得有效的steganography.
主要成果:
- 拟议的方法实现了DNN的无损稳态传输.
- 秘密模型的完整性和性能在整个过程中都被保留.
- 实验结果表明,高性能石图网络的安全和高效交付.
结论:
- 基于模型算法的方法为秘密DNN传输提供了安全有效的解决方案.
- 这种方法通过确保模型完整性来克服现有技术的局限性.
- 这些发现有助于确保机器学习模型的部署和通信.
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