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Cryptanalysis of Keystream Reuse in Stream Ciphered Digitized Speech using HMM based ASR Techniques

机译:基于HMM基于ASR技术的流加密数字化语音中的keyStream重用密码分析

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The keystream reuse problem in case of textual data has been the focus of cryptanalysts for quite some time now. This paper presents the use of hidden markov models based speech recognition approach to cryptanalysis of stream ciphered digitized speech in a keystream reuse situation. In this paper, we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream. The technique is flexible enough to incorporate all modern speech coding schemes and all languages for which the speech recognition techniques exist. The technique is simple and efficient and can be practically employed with the existing HMM based probabilistic speech recognition techniques with some modification in the training (pre-computation) and/or the maximum likelihood decoding procedure. The simulation experiments, though preliminary, showed promising initial results by recognizing about 80 percent correct phoneme pairs encrypted by the same keystream.
机译:在文本数据的情况下,keystream重用问题是现在的一段时间的密码分析焦点。本文介绍了基于隐马尔可夫模型的语音识别方法来密码分析了键入的重用情况下的流加密数字语音的密码分析。在本文中,我们展示了对手如何自动恢复在相同键盘下加密的数字化语音信号。该技术足够灵活,可以包含所有现代语音编码方案和所有语言存在的语音识别技术。该技术简单且有效,并且可以实际上与现有的基于HMM的概率语音识别技术,在训练(预计算)和/或最大似然解码过程中具有一些修改。虽然初步,仿真实验表明,通过识别由相同的键盘加密的大约80%正确的音素对,展示了初始结果。

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