敌对机器学习中的防御:从生命周期的角度进行系统调查
IEEE transactions on pattern analysis and machine intelligence
|September 17, 2025
概括
机器学习 (ML) 系统面临来自对抗性攻击的安全威胁. 这项调查统一了整个ML生命周期的防御策略,为强大的模型开发提供了新的视角.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习安全 机器学习安全
背景情况:
- 机器学习 (ML) 系统,特别是深度神经网络,表现出对抗现象,产生不一致和不可理解的预测.
- 这些漏洞带来了重大的安全风险,导致了后门攻击,权重攻击和对抗性例子等攻击范式的发展.
- 现有的防御机制往往是针对单个攻击类型的,这使得整体系统的稳定性评估具有挑战性.
研究的目的:
- 系统地审查ML系统中对抗对手攻击的现有防御范式.
- 为分析和分类防御方法提供统一的生命周期视角.
- 促进开发更全面,更先进的国防战略.
主要方法:
- 将ML系统分解为五个生命周期阶段:预培训,培训,后培训,部署和推断.
- 开发一个清晰的分类法,以分类每个阶段的代表性防御方法.
- 从统一生命周期的角度分析防御机制.
主要成果:
- 一个系统的审查和对抗ML对抗攻击的防御方法的分类.
- 一个统一的生命周期视角,阐明各种防御范式之间的联系和差异.
- 确定研究缺口,并为未来的全面国防战略提供灵感.
结论:
- 统一的生命周期方法对于理解和开发针对ML中各种对抗性攻击的强有力的防御至关重要.
- 拟议的分类学有助于分析现有防御,并指导未来的研究向更综合的解决方案.
- 这项工作为提高机器学习系统的安全性和可靠性提供了基础框架.
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