使用特征金字塔和非本地机制的普遍关注引导的对抗防御
Jiawei Zhao1, Lizhe Xie2, Siqi Gu1
1Southeast University, School of Cyber Science and Engineering, Nanjing, 210096, China.
Scientific reports
|February 12, 2025
概括
这项研究引入了新的防御模块,通过分析模型注意力来保护深度神经网络 (DNN) 免受敌对攻击. 拟议的通用防御战略显著提高了对手的强度,以最小的入侵和更低的成本.
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) 容易受到对抗的例子的影响,这限制了它们在安全应用中的使用.
- 现有的防御缺乏普遍性,只对特定类型的攻击有效.
- 通过模型注意力转移分析对抗性示例,提供了一个新的防御视角.
研究的目的:
- 开发针对DNN的对抗性攻击的通用防御模块.
- 根据它们对模型注意力的影响来分类攻击.
- 为了增强对手的稳定性,最小的模型入侵.
主要方法:
- 将对抗性攻击分为注意力转移和注意力减弱类别.
- 建议的特点基于金字塔的注意力空间引导 (FPAS) 模块用于注意力转移攻击.
- 开发了基于注意力的非局部 (ANL) 模块,用于注意力减弱攻击.
- 将FPAS和ANL模块集成到一个Wide-ResNet模型中,在一个增强框架内.
主要成果:
- 集成的FPAS和ANL模块展示了协同作用的防御能力.
- 观察到防御率的显著改善,即使对付嵌入补丁的对抗性示例.
- 拟议的通用防御战略实现了比基线平均5.47%和7.74%的防御率提升.
结论:
- 新的防御模块提供了对各种对抗性攻击的全面保护.
- 与主流方法相比,这种通用防御战略提供了一个具有成本效益的解决方案.
- 该方法可适应与现有防御系统集成,以进一步提高对抗性强度.
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