一种增量对抗训练方法可以及时和快速地获得新的知识
Yuxin Ge1, Yanhua Dong2, Hongyu Sun3
1College of Mathematics and Computer, Jilin Normal University, Siping, 136000, China.
Scientific reports
|October 14, 2025
概括
本研究引入了脑计算机接口 (BCI) 的增量对抗训练 (IncAT),增强了对攻击的深度模型稳定性,而不会影响清洁样本的准确性. 在BCI系统中,Incat方法提高了防御效率和知识保留.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 反对训练可以保护深度模型,但在计算上是昂贵的,阻碍了及时更新和新知识的整合.
- 现有的方法需要全面的神经网络再培训,这对像脑计算机接口 (BCI) 这样的动态应用提出了挑战.
研究的目的:
- 为BCI应用中的深度学习模型提出和评估一个增量对抗训练 (IncAT) 方法.
- 提高对抗攻击的模型稳定性,同时保持清洁数据的性能和提高学习效率.
主要方法:
- 为BCI开发了一个神经混合组装网络 (NHANet).
- 计算了费舍尔信息矩阵以确定重要的网络参数.
- 在对抗样本训练期间内置弹性重量巩固 (EWC) 损失,以保持关键参数.
主要成果:
- 应用Incat到波恩大学的BCI数据集.
- 实现了95.33% (FGSM),94.67% (PGD) 和93.60% (BIM) 的强大的精度.
- 与传统方法相比,显著提高了精度 (5.06%, 4.67%, 2.67%),但没有影响清洁样本的精度.
结论:
- 拟议的Incat方法在BCI中提供了有效和计算效率高的防御,以抵御BCI中的对抗性攻击.
- 在不牺牲清洁数据的性能的情况下,Incat成功地提高了模型的稳定性和通用性,验证了其有效性和效率.
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