基于潜伏表示指导的对抗性防御方法,用于远程传感图像场景分类.
Qingan Da1, Guoyin Zhang1, Wenshan Wang1
1College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种用于遥感图像的新型对抗性否定方法,增强了对抗性攻击的深度神经网络稳定性. 该技术有效地消除噪音,提高场景分类的准确性和安全性.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在遥感图像分析方面表现出色,但易受对抗性示例的影响.
- 敌对攻击对区域和生产安全构成风险,需要强大的防御机制.
- 现有的防御方法经常与未知的敌对噪音作斗争.
研究的目的:
- 为远程传感图像场景分类提出一个对抗性排斥方法.
- 为了提高DNN对抗对抗噪声的稳定性.
- 提高遥感图像分析的安全性和可靠性.
主要方法:
- 一个变化自编码器 (VAE) 经过训练,可以使用干净的数据集进行数据重建.
- 在测试时,规范化的相互信息指导选择性图像重建.
- 隐藏的表示是通过重建损失来代更新以消除对抗噪音.
主要成果:
- 拟议的方法在遥感图像场景分类方面表现出有效性.
- 与最先进的对抗防御方法相比,它实现了更强大的精度.
- 无噪声器,仅在清洁数据上训练,表现出对未知的对抗噪声的稳定性.
结论:
- 隐藏的表示指导对抗性否定方法对于遥感图像是有效的.
- 该方法提高了对抗攻击的稳定性,这对于安全关键应用至关重要.
- 这种方法为遥感中安全可靠的深度学习提供了有希望的方向.
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