针对交通标志的对抗性攻击的样本选择
Yiwen Wang1, Yue Wang1, Guorui Feng1
1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
概括
本研究介绍了针对交通信号对抗攻击的阶级增量学习方法. 它有效地选择代表性样本,以改善对自动驾驶系统的对抗性攻击生成.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 准确的交通标志识别对于自动驾驶和交通监控至关重要.
- 敌对攻击测试自动驾驶系统的安全性,并为算法改进提供信息.
- 随着交通基础设施的不断发展,新的交通标志类型需要适应性对抗性攻击模型.
研究的目的:
- 为交通标志对抗性攻击开发一个阶级增量学习方法.
- 为了使对抗性攻击模型能够适应新增的交通标志类别.
- 提高自动驾驶识别算法的安全测试和稳定性.
主要方法:
- 为交通标志对抗性攻击提出了一个阶级增量学习方法.
- 使用Pinpoint区域概率估计网络 (PRPEN) 在现有样本中识别高概率攻击区域.
- 构建一个重复样本集,优先考虑具有较小高概率像素度区域的旧样本,以实现增量学习.
主要成果:
- 与现有方法相比,拟议的方法选择了更具代表性的样本.
- 能够更有效地训练PRPEN生成准确的概率图.
- 在增量学习框架内,对交通标志进行了改进的对抗性攻击生成.
结论:
- 开发的类增量学习方法提高了对抗性攻击模型适应新交通标志类的适应性.
- 优先考虑特定样本可以提高训练对抗性攻击模型的效率和有效性.
- 为更强大的安全评估和自动驾驶系统的改进做出贡献.
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