提高具有特征干扰的普遍对抗性扰乱的可转移性
概括
这项研究引入了一种新的通用对抗性攻击 (UAP),通过破坏模型不可知特征来增强深度神经网络安全性. 这种新的方法在不同的模型和数据集中实现了强大的可转移性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 容易受到普遍对抗性干扰 (UAP) 的影响.
- 现有的UAP方法在可转移性 (数据依赖) 或性能 (数据独立) 上有局限性.
研究的目的:
- 提出一种新的通用对抗性攻击方法,用于生成具有增强可转移性的UAP.
- 解决当前数据依赖和数据独立的UAP方法的局限性.
主要方法:
- 设计了一个客观函数来破坏模型不可知特征,削弱显著的和加强不那么重要的道明智的特征.
- 用于培训UAP的OOD数据,消除对标记样本的依赖.
- 采用小型批量输入梯度和代UAP更新的势头术语,捕获本地和全球信息.
主要成果:
- 与现有的UAP方法相比,拟议的方法显示出更高的性能.
- 在各种模型,数据集和任务中实现了UAP的强大的可转移性.
- 有效地破坏了强大的对抗性攻击的模型不可知特征.
结论:
- 新的UAP生成方法提供了更好的性能和可转移性.
- 这种方法提高了对DNN的对抗性攻击的理解和缓解.
- 这些发现有助于开发更强大的深度学习模型.
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