在敌对攻击下提高意图检测的稳定性:从几何约束的角度来看
IEEE transactions on neural networks and learning systems
|August 11, 2023
概括
本研究引入了一种通过改进特征表示来对抗自然语言处理 (NLP) 中的对抗性示例的新防御方法. 拟议的几何约束方法提高了用于意图检测任务的模型稳定性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 对于自然语言处理 (NLP) 的深度神经网络 (DNN) 容易受到对抗的例子的影响.
- 现有的防御方法经常使用线性分类器和软max,它们不促进分离良好的特征表示,使模型变得脆弱.
- 对话系统中的意图检测是NLP的一个关键任务,缺乏对此类攻击的强有力的防御机制.
研究的目的:
- 提出一种简单而有效的防御方法,以增强NLP系统对抗对抗实例的稳定性.
- 改进DNN中的特征表示学习,以便更好地区分类别.
- 解决当前防御策略在意图检测任务中的局限性.
主要方法:
- 引入了一种M相似度指标,以减少类内特征的变异,旨在在特征空间中实现更好的几何条件.
- 根据理论保证的整体错误分类概率 (OMP) 来推导类别的最佳几何约束.
- 制定了几何约束作为Stiefel多元体上的多元优化问题,以克服传统的优化挑战.
主要成果:
- 拟议的方法显著提高了NLP模型与对抗性示例的稳定性.
- 该方法在非对抗性 (正常) 例子上保持了出色的性能.
- 实验结果显示,与现有的基线防御方法相比,性能优越.
结论:
- 拟议的几何约束防御方法为提高NLP系统的弹性提供了一个有希望的解决方案,特别是在意图检测方面.
- 通过多元优化优化特征空间几何学是对抗对抗攻击的有效策略.
- 这项工作有助于建立更可靠和安全的对话系统.
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