GHAttack:对异质图神经网络的生成对抗性攻击
IEEE transactions on neural networks and learning systems
|January 13, 2026
概括
本研究介绍了生成异质攻击 (GHAttack),这是有效攻击异质图形神经网络 (HGNN) 的新方法. GHAttack很快就会产生干扰,这使得对HGNN的对抗性攻击变得更加实用.
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:
- 不同质的图形神经网络 (HGNNs) 越来越多地使用,但容易受到对抗性攻击.
- 目前的HGNN攻击方法在计算上昂贵,限制了它们在推理过程中的使用.
研究的目的:
- 为HGNNs开发一个高效和有效的对抗性攻击方法.
- 解决现有的HGNN攻击策略的计算效率低下问题.
主要方法:
- 引入了生成异质攻击 (GHAttack),一种新的生成攻击方法.
- 开发了一个通过优化问题进行训练的扰动发生器,利用HGNN骨干和关系意识输出层.
- 启用扰动来修改异质图关系中的边缘,以提高攻击效率.
主要成果:
- 在实验中,GHAttack表现出高效率和卓越的有效性.
- 通过十个代表性的HGNN和六个数据集进行验证.
- 生成式方法允许通过简单的前进传递快速产生扰动.
结论:
- 对于对HGNN的对抗性攻击,GHAttack提供了一个计算效率高的解决方案.
- 该方法有效地通过扰乱图形结构来降低HGNN性能.
- 这项工作推进了基于图形的机器学习模型的对抗性强度领域.
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