通过曲率感知惩罚增强对抗性可转移性
Cheng Peng1, Zeze Tao1, Junyu Liu1
1School of Cyber Security and Computer, Hebei University, Hebei, China.
概括
本研究介绍了曲率意识惩罚 (CAP),一种基于转移的新型攻击方法,通过保持更平坦的损失表面来增强对手示例的可转移性和对防御的稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
背景情况:
- 基于转移的攻击产生了替代模型的对抗性例子,利用可转移性来欺骗未知的模型.
- 通过最小化最大损失来优化损失表面是具有挑战性的,因为在后来的攻击阶段的非线性景观.
研究的目的:
- 提出一种新的攻击方法,曲率意识惩罚 (CAP),以解决后来的攻击阶段梯度上升的无效性.
- 在对抗性攻击生成期间保持损失表面的平坦度.
主要方法:
- CAP结合了梯度规范和曲率感知术语作为调整,以保持损失表面平坦度.
- 使用有限差异法来近似赫西矩阵,降低计算成本.
- 平均静态梯度样本以减少方差和近似的赫西安.
主要成果:
- 在CAP中,CAP创建了具有跨多种网络架构增强可转移性的对抗性示例.
- 拟议的CAP攻击显示出对最先进的对抗性防御方法的更强的抵抗力.
- 实验结果验证了CAP在提高对抗攻击性能方面的有效性.
结论:
- 曲率意识惩罚 (CAP) 提供了一种有效的方法,可以生成更可转移和更强大的对抗性示例.
- 该方法改进了现有技术,解决了对抗性攻击中损失景观的非线性.
- CAP在对抗性机器学习和模型安全领域提供了实际的进步.
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