强大的长尾识别与分布意识的对抗性示例生成
Bo Li1, Yongqiang Yao2, Jingru Tan3
1Tongji University, No. 4800 Caoan Road, Shanghai, 201804, Shanghai, China.
概括
本研究介绍了分布意识对抗示例生成 (DAG),以提高长尾分布中的对抗稳定性. DAG平衡了对抗性示例生成,提高了代表性不足的阶级的表现.
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:
- 敌对攻击对机器学习模型构成重大威胁.
- 在长尾数据分布下实现对抗性稳定性是具有挑战性的.
- 现有的对抗性训练方法往往忽视了对抗性示例生成中的不平衡.
研究的目的:
- 调查长尾分布对对抗性示例生成阶段的影响.
- 提出一种新的方法,在不同类别之间平衡对抗性示例生成.
- 为了增强在不平衡数据集上训练的模型的对抗性稳定性.
主要方法:
- 拟议的分布意识对抗示例生成 (DAG) 方法.
- 引入了虚拟示例创建器 (VEC) 来平衡对抗性干扰.
- 使用梯度导向校准器 (GGC) 专注于基于生成质量的尾部类.
主要成果:
- 证明对抗性示例生成质量对于尾部类来说较差.
- DAG有效地在头部和尾部类之间平衡对抗性示例生成.
- 与现有方法相比,在长尾对抗性基准上取得了更好的表现.
结论:
- 提出的DAG方法有效地解决了长尾分布的对抗性示例生成的不平衡.
- DAG显著提高了对抗性稳定性,特别是在代表性不足的阶级.
- DAG代表了对不平衡数据集的强大机器学习的实质性进步.
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