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从模糊的角度重新思考可转移的对抗性示例的优化目标.

Xiangyuan Yang1, Jie Lin1, Hanlin Zhang2

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.

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概括
此摘要是机器生成的。

可转移的对抗性攻击难以转移到新的模型. 新的模糊域优化 (FOTA) 和自适应FOTA (Ada-FOTA) 方法显著提高了对不熟悉模型的对抗性示例可转移性.

关键词:
对抗性的例子.模糊的域名 模糊的域名模糊优化优化 模糊优化可转让性 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
  • 网络安全 网络安全

背景情况:

  • 基于转移的攻击产生了对不熟悉的模型有效的对抗性示例.
  • 当前的方法在将轻微干扰转移到各种受害者模型中面临着挑战.
  • 不能转移的例子具有定义的"模糊域"的特点.

研究的目的:

  • 开发新的方法来提高对抗性示例的可转移性.
  • 解决现有的基于转移的攻击在破坏不熟悉的模型中的局限性.
  • 提高对未见深度学习模型的对抗性攻击的成功率.

主要方法:

  • 引入了"模糊域"的概念,以表征不可转移的对抗性例子.
  • 建议基于模糊优化的可转移攻击 (FOTA),以最大限度地提高交叉损失和会员功能.
  • 开发了适应FOTA (Ada-FOTA) 来进行动态对抗例更新,以最大限度地提高可转移性.

主要成果:

  • 在ImageNet上,FOTA提高了5.4%的对抗性示例可转移性,而不是自然训练的模型.
  • 与现有方法相比,Ada-FOTA进一步提高了可转移性13.8%.
  • 提出的方法在攻击未见的受害者模型方面取得了显著的改进.

结论:

  • FOTA和Ada-FOTA有效地提高了对抗性示例的可转移性.
  • 这些方法为攻击不熟悉的模型提供了强大的解决方案,具有轻微的干扰.
  • 模糊域概念为理解和改进对抗性攻击提供了一个新的视角.