动态路由和知识重新学习用于无数据黑盒攻击
IEEE transactions on pattern analysis and machine intelligence
|September 27, 2024
概括
本研究介绍了DraKe,这是一种针对深度学习模型的无数据黑子对抗性攻击的新框架. 德拉基动态学习替代模型,在攻击各种目标模型时优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型很强大,但对对抗性示例很脆弱.
- 现有的无数据黑盒攻击存在局限性,包括静态替代模型和依赖目标模型数据统计.
研究的目的:
- 提出一个新的动态路由和知识再学习框架 (DraKe),用于有效的无数据黑子对抗性攻击.
- 通过实现动态替代模型学习和知识适应来解决以前方法的局限性.
主要方法:
- 开发了一个动态的替代结构学习策略,使用政策网络来适应替代模型的不同目标.
- 实现基于图形的结构信息学习,以从目标模型中获取知识.
- 引入了一个动态的知识再学习策略,通过重新学习硬样本来完善替代模型.
主要成果:
- 在图像分类和面部识别基准方面,DraKe在最先进的竞争对手上取得了显著的改进.
- 该框架在各种目标模型中展示了一致的攻击优势,包括剩余网络和视觉变压器.
结论:
- DraKe提供了一种更实用,更有效的方法来对付无数据黑子对抗性攻击.
- 德拉基的动态和适应性显示了涉及深度学习安全的真实应用的巨大潜力.
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