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相关实验视频

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Revealing Neural Circuit Topography in Multi-Color
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对图形神经网络的解释子图攻击.

Huiwei Wang1, Tianhua Liu2, Ziyu Sheng3

  • 1College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China; The Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing, Chongqing, 400715, China.

Neural networks : the official journal of the International Neural Network Society
|January 29, 2024
PubMed
概括

图形神经网络 (GNN) 的解释方法可以被利用进行攻击. 这项研究表明,解释子图如何用于对GNN模型的逃避和后门攻击.

关键词:
敌对的攻击是敌对的攻击.后门攻击是通过后门进行的.可以解释的可解释性.图形神经网络的神经网络

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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) 缺乏透明度,阻碍了关键应用程序.
  • 现有的GNN解释方法提供了洞察力,但可能引入安全漏洞.
  • 在GNN中解释性对于信任和安全至关重要.

研究的目的:

  • 调查与GNN解释方法相关的安全风险.
  • 提出新的逃避和后门攻击策略,利用解释性的子图.
  • 评估这些攻击的有效性和特征.

主要方法:

  • 使用GNN解释方法SubgraphX来获得本地解释子图.
  • 通过替换解释子图来诱导错误分类,开发了逃避攻击.
  • 使用解释性触发器和战略注入点设计后门攻击.

主要成果:

  • 证明了针对最先进的 GNN 模型提出的逃避和后门攻击的有效性.
  • 在各种数据集中验证了攻击.
  • 展示了拟议的后门攻击的增强效率,适应性和隐藏性.

结论:

  • 虽然有助于解释性,但GNN解释方法可以被武器化.
  • 利用解释子图为GNN提供了一个可行的攻击向量.
  • 拟议的攻击突显了在可解释的GNN中需要强有力的安全措施.