基于统计分析和深度神经网络的VoIP级分析方法的评估
Hojat Allah Moghadasi1, Hamid Dehghani2
1Faculty of Electrical & Computer Engineering, Malek Ashtar University of Technology, Tehran, Iran.
Scientific reports
|December 8, 2025
概括
本研究介绍了一种混合人工智能和信号处理方法,用于检测语音互联网协议 (VoIP) 呼叫中的隐形图形. 先进的技术准确地识别音频中隐藏的数据,提供高效的实时安全解决方案.
科学领域:
- 网络安全 网络安全
- 数字信号处理 数字信号处理
- 人工智能的人工智能
背景情况:
- 语音通过互联网协议 (VoIP) 越来越多地用于隐蔽的隐蔽摄影.
- 现有的稳定分析方法在高精度和高效率方面存在困难.
- 集成信号处理和机器学习提供了增强的检测能力.
研究的目的:
- 提出一种新的混合方法,将语音信号处理和人工智能结合起来,用于VoIP隐形识别检测.
- 为了评估拟议的方法的准确性和计算效率与已知的石学技术相比.
- 为了证明模型对实时阶段分析应用的有效性.
主要方法:
- 应用数据预处理到G.729压缩音频信号,以提取框架内特征和框架间相关性.
- 使用深度学习网络进行培训,以区分覆盖和stego数据.
- 评估了混合方法与量化指数调制 (QIM),音调调制稳定图 (PMS) 和异质并行稳定图 (HPS) 的对比.
主要成果:
- 混合方法在检测精度和计算效率方面取得了显著的改进.
- 获得了高准确率:QIM的98.85%,PMS的96.94%和HPS的91.90%.
- 对于1000ms的音频文件,steganalysis测试的响应时间低于5ms,这表明速度很快.
结论:
- 拟议的混合人工智能和信号处理技术为VoIP隐形图检测提供了与传统方法相比更高的准确性和效率.
- 该模型的高速性能使其适合实时级分析.
- 这项研究为应对VoIP通信中隐形图形构成的安全威胁提供了强有力的解决方案.
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