基于事件的原始对抗攻击,用于具有可配置延迟的尖端神经网络
Xiao Du1, Wanli Shi2, Xiaohan Zhao3
1School of Mathematics, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, Jilin, China.
概括
这项研究引入了使用事件流数据的尖端神经网络 (SNN) 的新对抗攻击. 该方法有效地针对具有可调节延迟的原始事件流,提高节能边缘设备的安全性.
科学领域:
- 人工智能
- 计算机视觉
- 神经形态计算
背景情况:
- 尖端神经网络 (SNN) 和动态视觉传感器 (DVS) 为边缘设备提供节能计算.
- 在SNN中输入延迟对于节能至关重要,但安全漏洞,特别是对抗性攻击,越来越令人担忧.
- 由于数据稀疏性和离散优化挑战,对具有可配置延迟的原始事件流的攻击未被充分探索.
研究的目的:
- 提出一种针对可配置延迟的SNN中的原始事件流的新型对抗性攻击方法.
- 在原始事件流攻击中解决稀疏性和离散优化的挑战.
- 加强节能边缘人工智能系统的安全性.
主要方法:
- 通过将二进制峰值转换为连续值, 开发了一种优化方法.
- 引入了一个适应性随机策略来采样攻击延迟.
- 应用规范化以保持稀疏性并确保对抗样本与原始数据相似,同时接近目标标签.
主要成果:
- 拟议的方法在不同延迟时间的对抗性攻击成功率 (ASR) 中明显优于现有的方法.
- 在N-MNIST,CIFAR10-DVS,N-Caltech-101和Gesture-DVS数据集上的实验显示出一致的优势.
- 废弃研究证实了拟议技术的有效性和延迟对对抗样本产生的影响.
结论:
- 这种新型攻击方法有效地针对具有可配置延迟的SNN中的原始事件流.
- 这种方法提高了基于事件的视觉系统的安全性,防止对手操纵.
- 这项研究为了解神经形态系统的攻击和防御开辟了新的途径.
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