动态PAE:实时生成现场意识的物理对抗实例
IEEE transactions on pattern analysis and machine intelligence
|October 28, 2025
概括
本研究介绍了DynamicPAE,这是一种用于实时物理对抗攻击的新框架. 它通过启用现场意识攻击来增强深度学习安全性,显著优于静态方法.
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:
- 物理对抗实例 (PAE) 突出了深度学习中的真实风险.
- 目前的PAE世代缺乏适应多样化,动态场景的能力.
- 需要实时,观察条件下的动态PAE.
研究的目的:
- 为现场意识,实时物理对抗性攻击 (DynamicPAE) 开发第一个生成框架.
- 在攻击训练期间在杂的反下解决学习稀疏关系的挑战.
- 为了使生成的PAE与现实世界的场景保持一致,以进行有效的物理攻击.
主要方法:
- 引入了剩余引导的对抗模式探索,以克服噪音反.
- 模拟的训练退化与有限的反信息限制.
- 拟议的分布匹配攻击场景调整,包括条件不确定性调整数据和偏差调整目标重权.
主要成果:
- 动态PAE在数字和物理评估中展示了优越的攻击性能.
- 与物体探测器相比,实现了2.07倍的提升和58.8%的平均AP下降.
- 超越了最先进的静态PAE生成方法.
结论:
- 动态PAE是第一个允许对动态PAE进行端到端建模的框架.
- 提出的方法有效地解决了噪音反和场景调整挑战.
- 动态PAE显著提升了实时物理对抗攻击的能力.
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