通过特征混合和超级学习探索频率,以提高对手的可转移性
概括
本研究介绍了一种基于频率的特征混合方法,以改善对深度神经网络 (DNN) 的对抗性攻击. 该方法通过战略性地结合来自清洁和对抗样本的特征,提高了对正常和防御模型的攻击可转移性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习安全 机器学习安全
背景情况:
- 深度神经网络 (DNN) 容易受到敌对攻击.
- 频域分析揭示了高频组件的影响预测,而低频组件有助于黑子攻击的可转移性.
研究的目的:
- 开发基于频率分解的特征混合方法,以利用频率特征来改进对抗性攻击.
- 解决因同时应用不同特征混合策略而产生的冲突.
主要方法:
- 引入了一种基于频率分解的特征混合方法.
- 提出了一种跨频度元优化方法,包括元训练,元测试和最终更新步骤.
- 杆低频组件用于防御模型攻击和对抗样本用于正常训练模型攻击.
主要成果:
- 在正常训练模型中,将清洁样本特征纳入对抗特征是有效的.
- 将清洁特征与低频对抗特征相结合,可以改善对防御模型的攻击.
- 交叉频率元优化方法有效地提高了针对这两种模型类型的攻击可转移性.
结论:
- 提出的基于频率的特征混合和元优化方法显著提高了对抗性攻击的可转移性.
- 该研究展示了一种利用频率特征进行更有效的DNN攻击的新方法.
- 该方法在正常训练和防御模型的 ImageNet-Compatible 数据集上显示出有效性.
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