适应性扰乱对敌对攻击的攻击
IEEE transactions on pattern analysis and machine intelligence
|February 20, 2024
概括
研究人员提出了一种新方法,用于生成针对深度学习模型的对抗性示例. 这种方法通过使用精确的梯度方向而不是标志函数来提高攻击成功率和黑子可转移性.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
背景情况:
- 深度学习模型越来越容易受到对抗性示例的影响.
- 当前基于梯度的攻击通常使用标志函数,它可以改变梯度方向.
研究的目的:
- 开发一种更有效的方法来生成对抗性示例.
- 为了提高攻击成功率和对抗攻击的可转移性.
主要方法:
- 提出一种基于梯度的新型攻击方法,该方法利用了精确的梯度方向与缩放因子.
- 引入一个自适应缩放因子生成器,以优化单个图像的扰动.
- 从理论上证明了黑子可转移性的改进.
主要成果:
- 拟议的方法增强了对抗性示例生成,以更少的干扰实现更高的攻击成功率.
- 对CIFAR10和ImageNet数据集的实验表明,与最先进的方法相比,它们具有更高的可转移性.
- 适应性缩放因子生成器可以减少计算开销.
结论:
- 使用精确的梯度方向与自适应缩放比标志函数更有效地生成对立的例子.
- 这种方法在对抗性攻击性能和可转移性方面提供了显著的改进.
- 这种方法与现有的基于梯度的攻击技术兼容.
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