一种基于多数逻辑的新模糊方法,用于防止对二进制深度神经网络的未经授权访问.
Alireza Mohseni1, Mohammad Hossein Moaiyeri2, Mohammad Javad Adel1
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran.
Scientific reports
|July 8, 2025
概括
本研究介绍了一种基于密钥的硬件和软件联合设计,以保护深度神经网络 (DNN) 模型. 该方法通过使用不正确的密钥降低模型准确度来阻止未经授权的访问,从而增强二进制神经网络 (BNN) 的安全性.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络安全 网络安全
背景情况:
- 深度学习模型,特别是深度神经网络 (DNN),是越来越有价值的资产,需要对未经授权的访问进行保护.
- 二元神经网络 (BNN) 的兴起为硬件实现和安全带来了独特的挑战和机会.
研究的目的:
- 提出一种新的基于密钥的算法-硬件联合设计方法来保护DNN模型.
- 制定专门针对BNN量身定制的保护策略,同时确保其广泛适用于其他神经网络加速器.
主要方法:
- 开发了一种创新的基于密钥的算法-硬件联合设计方法,以保护DNN模型.
- 该方法通过使用Cadence Virtuoso在TSMC 40nm CMOS技术上的布局后模拟来验证该方法.
- 对各种攻击进行了安全评估,包括布尔满足性,结构性,逆向工程和侧通道攻击.
主要成果:
- 拟议的方法大大降低了模型准确性与错误的密钥,有效地防止未经授权的访问.
- 该方法在不同BNN架构和数据集的现有解决方案相比,显示出更高的效率.
- 该设计实现了面积 (43%),平均功率 (79%) 和重量修改能量 (71%) 的大幅减少.
结论:
- 基于密钥的共同设计方法为保护DNN模型,特别是BNN提供了强大的和高效的解决方案.
- 这种方法可以增强神经网络加速器对各种网络威胁的硬件安全性.
- 经过验证的设计在资源利用和能源效率方面提供了显著的改进.
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