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FDAA:一种特征分布意识的可转移的对抗性攻击方法.

Jiachun Li1, Yuchao Hu1, Cheng Yan1

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society
|June 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种特征分布意识的可转移对抗性攻击 (FDAA),以改进对未知的深度神经网络的攻击. 该方法增强了特征地图的拒绝和输入完整性,以实现更有效的对抗性攻击.

关键词:
敌对的攻击是敌对的攻击.聚合特征地图 聚合特征地图深度神经网络是一种深度神经网络.具有特征分布意识的特征分布.图像增强 图像增强 图像增强可转让性 可转让性

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科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 可转移的对抗性攻击旨在欺骗未知的深度神经网络.
  • 目前的方法与特征地图噪声,增强信息丢失和特征分布意识作斗争.

研究的目的:

  • 提出一种基于特征分布的可转移对抗性攻击 (FDAA) 方法.
  • 提高对未知的深度神经网络的对抗性攻击的可转移性.

主要方法:

  • 开发了一种新的聚合特征地图攻击 (AFMA) 用于特征地图排斥.
  • 引入了一个Smixup输入转换策略,以保持功能完整性.
  • 基于特征分布,为不同的图像区域实施了不同的策略.

主要成果:

  • 拟议的FDAA方法显著提高了攻击的可转移性.
  • 在对抗训练的模型中,平均成功率为78.6%.
  • 在消除特征地图和捕获综合特征方面已证明有效.

结论:

  • FDAA解决了现有的可转移对抗性攻击的局限性.
  • 该方法提供了一种更强大的方法,用于为未知模型生成对抗性示例.
  • 对特征分布的认识对于提高对抗性攻击的可转移性至关重要.