通过热带几何形状的逆向强大的神经网络决策边界
Kurt Pasque1, Christopher Teska1, Ruriko Yoshida1
1Naval Postgraduate School, Department of Operations Research, 1411 Cunningham Road, Monterey, 93943, CA, USA.
概括
我们开发了一种新的热带卷积神经网络架构,可以增强对手的稳定性. 这种计算效率高的方法可以提高神经网络对攻击的安全性,并且训练时间最小.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 敌对攻击对神经网络的可靠性构成重大威胁.
- 像对抗训练这样的现有防御方法在计算上昂贵.
- 零碎的线性神经网络具有固有的热带性质.
研究的目的:
- 引入一种新的,计算效率高的热带卷积神经网络架构.
- 增强神经网络对抗敌对攻击的稳定性.
- 研究热带神经网络中决策边界的几何性质.
主要方法:
- 使用热带嵌入层将数据嵌入热带投射.
- 将热带嵌入层集成到现有的神经网络架构中.
- 分析决策边界的几何和线性区域的数量.
主要成果:
- 拟议的热带卷积神经网络架构展示了最先进的对抗性强度.
- 热带嵌入层增加了决策边界中线性区域的数量.
- 与对抗训练相比,该方法需要的计算时间要少得多.
结论:
- 热带嵌入层提供了一个简单,高效和有效的防御对抗对手的攻击.
- 这种方法为构建更安全和更强大的深度学习模型提供了新的途径.
- 热带卷积神经网络架构为实际的对抗防御提供了一个有希望的替代方案.
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