引导DE:通过对自动语音识别系统的信任引导突变进行有针对性的黑子对抗性攻击
Jing Li1, Lipeng Song2, Yanru Feng3
1The School of Computer Science and Technology, North University of China, Taiyuan, 030051, China.
概括
我们开发了指导差异进化 (Guided DE),以改善对自动语音识别 (ASR) 系统的黑子对抗性攻击. 这种方法显著降低了查询消耗,并揭示了用于更强大的ASR安全性的语音漏洞.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 在自动语音识别 (ASR) 中的深度神经网络 (DNN) 容易受到黑子对抗性攻击.
- 现有的基于遗传算法 (GA) 的方法受到高查询消耗和有限的机械分析的影响.
研究的目的:
- 为ASR系统开发一种更有效,更有洞察力的黑子对抗性攻击方法.
- 为了减少查询消耗,提高对抗性攻击的成功率.
- 发现ASR系统中的漏洞模式.
主要方法:
- 建议的指导差异演变 (Guided DE),一个框架,将差异演变 (DE) 与基于信心的梯度信号相结合.
- 综合基于人口的搜索与连接主义时间分类 (CTC) 损失反.
- 在三个ASR架构上评估了指导DE.
主要成果:
- 导向DE在40k-48.9k查询的两个词攻击中实现了90-95%的成功率,比GA基线提高了67-73%的效率.
- 与GA基线相比,成功概率增加了2.1-3倍.
- 确定能量丰富的语音短语对攻击更容易受到攻击31%.
- 与标准DE相比,指导DE的攻击成功率提高了62.4%,查询成本降低了19.3%.
结论:
- 导向DE为黑盒ASR攻击提供了一个高效的梯度演变融合框架.
- 这项研究揭示了与光谱能量动态相关的语音脆弱性模式.
- 这些发现有助于开发更强大,更安全的ASR系统,通过为进攻和防御策略提供信息.
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