不定位的白盒对抗性攻击,以突破深度倾斜的基于COVID-19监控的面罩检测系统
Burhan Ul Haque Sheikh1, Aasim Zafar1
1Department of computer science, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002 India.
概括
深度学习面具检测系统容易受到敌对攻击. 一个新的强大的框架显著提高了对这些攻击的检测准确性,提高了COVID-19监控系统的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 面罩检测系统对于控制COVID-19传播至关重要.
- 当前的深度学习模型容易受到敌对攻击,从而损害了它们的可靠性.
- 敌对攻击可以显著降低人工智能系统的性能.
研究的目的:
- 开发一种强大的面具检测系统,能够抵抗敌对攻击.
- 评估拟议框架对抗对抗操纵的有效性.
- 增强人工智能支持的公共卫生监测的安全性和可靠性.
主要方法:
- 在定制数据集上微调了MobileNetv2模型,用于面罩检测.
- 实施快速渐变信号方法 (FGSM) 来生成对抗性示例.
- 开发并应用一个强大的框架来提高模型对攻击的弹性.
主要成果:
- 最初的MobileNetv2模型在清洁数据上实现了95.83%的准确性.
- FGSM攻击将模型的准确性降低到14.53%.
- 强大的框架将对抗数据的准确性提高到92%,在清洁数据上略有下降到92.79%.
结论:
- 深度学习面具检测系统容易受到敌对攻击.
- 拟议的强大框架显著提高了对敌对攻击的抵抗力.
- 这项研究强调了在公共卫生应用中需要安全的人工智能.
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