D4A:在无神论的对抗性攻击中提供高效有效的防御
Xianxian Li1, Zeming Gan2, Yan Bai3
1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China; Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, China.
概括
图形神经网络 (GNN) 面临结构对抗性攻击. 我们引入了一种新的防御,使用无的消息传递和分发转移约束来提高对这些攻击的稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 越来越容易受到结构对抗性攻击的影响.
- 现有的防御缺乏对各种攻击和图形属性的强度.
- GNN 漏洞源于局部图形平滑和称为不合适的现象.
研究的目的:
- 为了研究在结构扰动下GNN的异常行为.
- 在图形数据中开发一个强大的防御机制来对抗结构对抗性攻击.
- 为了减轻"不合适"现象和GNN的后部分布转移.
主要方法:
- 从后部分布的角度分析GNN异常行为.
- 建议无传递消息,以提高干扰耐受性和减轻不合适的情况.
- 引入分配转移约束,以防止其他异常行为.
主要成果:
- 拟议的方法显著提高了在各种攻击中防御性能.
- 六个数据集的实验结果表明,与11个基线相比,其强度更高.
- 该方法在准确性和对抗性稳定性之间实现了有利的权衡.
结论:
- 新的防御战略有效地解决了GNN的结构脆弱性.
- 无的消息传递和分配转移约束提供了内在的保护.
- 这些发现为GNN在对抗性环境中提供了更强大,更可靠的方法.
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