蒙面空间光谱自编码器是优秀的高光谱防御器
IEEE transactions on neural networks and learning systems
|January 1, 2024
概括
这项研究引入了一个面具空间光谱自编码器 (MSSA) 来保护高光谱图像 (HSI) 分析系统免受敌对攻击. 该方法通过自主监督学习提高了稳定性,提高了HSI分类准确性和防御能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 深度学习 (DL) 已经推进了高光谱图像 (HSI) 分析,但引入了对敌对攻击的脆弱性.
- 现有的防御策略往往在有限的标记数据和可转移性方面扎.
- 在HSI分析中的稳定性对于可靠的应用程序至关重要.
研究的目的:
- 提出一种新的防御机制,即面具空间光谱自编码器 (MSSA),以增强HSI分析系统对抗对方攻击的稳定性.
- 提高HSI分析系统的固有稳定性和空间防御能力.
- 为了应对有限的标记样本的挑战,并提高HSI分析中的防御可转移性.
主要方法:
- 在自我监督学习理论下,开发了一个面具空间光谱自编码器 (MSSA) 框架.
- 一个蒙面序列注意力学习 (MSAL) 模块增强了光谱通道的稳定性.
- 一个具有可学习图形结构的图形卷积网络 (GCN) 便于为空间防御提供全球像素智能组合.
主要成果:
- 拟议的MSSA方法在提高HSI分析系统的稳定性方面取得了重大改进.
- 与最先进的超谱分类方法和对抗防御策略相比,实验显示出更高的性能.
- 以光谱重建为借口任务的自我监督方法提高了防御的可转移性,并处理有限的标记数据.
结论:
- MSSA有效地提高了HSI分析系统对抗敌对攻击的稳定性.
- 提出的方法为安全可靠的HSI分析提供了一个有希望的解决方案,特别是在数据稀缺的情况下.
- 在超光谱成像应用中,MSSA代表了对抗防御的重大进步.
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