提高基于利普希茨约束和通道激活抑制的ECG分类的对抗性稳定性
Xin Chen1,2, Yujuan Si1,2, Zhanyuan Zhang1,2
1School of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
用于心电图 (ECG) 分析的深度神经网络 (DNN) 容易受到对抗性攻击. 拟议的道激活抑制与利普希茨约束网 (CASLCNet) 增强了医学诊断中的对抗性稳定性.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 信号处理 信号处理
背景情况:
- 深度神经网络 (DNN) 对于心电图 (ECG) 信号诊断至关重要.
- DNN对对抗性示例具有脆弱性,在医疗应用中构成风险.
- 现有的方法难以平衡对抗攻击的准确性和稳定性.
研究的目的:
- 开发一个新的网络,CASLCNet,以提高DNN在ECG诊断中的对抗性稳定性.
- 解决DNN在医疗诊断任务中的对抗性干扰的脆弱性.
- 提高人工智能驱动的心电图分析的可靠性.
主要方法:
- 建议使用利普希茨约束网 (CASLCNet) 进行通道激活抑制.
- 实施了通道智能激活抑制 (CAS) 策略,以调节通道贡献.
- 使用1-Lipschitz的l∞距离网络进行强大的分类.
主要成果:
- 在PGD攻击下,CASLCNet在MIT-BIH数据集上实现了91.03%的ACC稳定.
- 在PGD攻击下,CASLCNet在CPSC2018数据集上实现了83.01%的ACCrobust.
- 在保持高诊断准确度的同时,证明了增强的对抗性稳定性.
结论:
- CASLCNet有效地提高了用于ECG信号分析的DNN的对抗性稳定性.
- 拟议的方法增强了对抗对抗干扰的模型安全性.
- CASLCNet为基于人工智能的可靠医疗诊断提供了一个有前途的解决方案.
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