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Steganalysis with low embedding rates is still a challenge in the field of information hiding. Speech signals are typically processed by wavelet packet decomposition, which is capable of depicting the details of signals with high accuracy. A steganography detection algorithm based on the Markov bidirectional transition matrix (MBTM) of the wavelet packet coefficient (WPC) of the second-order derivative-based speech signal is proposed. On basis of the MBTM feature, which can better express the correlation of WPC, a Support Vector Machine (SVM) classifier is trained by a large number of Least Significant Bit (LSB) hidden data with embedding rates of 1%, 3%, 5%, 8%,10%, 30%, 50%, and 80%. LSB matching steganalysis of speech signals with low embedding rates is achieved. The experimental results show that the proposed method has obvious superiorities in steganalysis with low embedding rates compared with the classic method using histogram moment features in the frequency domain (HMIFD) of the second-order derivative-based WPC and the second-order derivative-based Mel-frequency cepstral coefficients (MFCC). Especially when the embedding rate is only 3%, the accuracy rate improves by 17.8%, reaching 68.5%, in comparison with the method using HMIFD features of the second derivative WPC. The detection accuracy improves as the embedding rate increases.

To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM (complementary neighbor vertices-quantization index modulation) steganography in low bit-rate speech codec, the code-word correlation model based on a BiLSTM (bi-directional long short-term memory) neural network is built to obtain the correlation features of the LPC codewords in speech codec in this paper. Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams. The experimental results show that for speech steganography of short samples with low embedding rate, the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods of the RNN-SM (recurrent neural network-steganalysis model) and SS-QCCN (simplest strong quantization codeword correlation network). At an embedding rate of 20% and a duration of 3 s, the detection accuracy of BiLSTM method reaches 75.7%, which is higher than that of RNN-SM by 11.7%. Furthermore, the average testing time of samples (100% embedding) is 0.3 s, which shows that the method can realize real-time steganography detection of VoIP streams.

With the development of modern science and technology, the development of electrical automation engineering has also become widespread. In the traditional sense, the wiring of the electrical automation control system is complex, the cost is high, the maintenance is difficult and failures may easily occur, and these factors seriously affect the system efficiency. The application of PLC technology in the field of electrical automation greatly simplifies the installation and operation process, and improves the work efficiency. Therefore, PLC technology has come to play an increasingly important role in the efficient and stable operation of electrical automation engineering. This paper analyses and explores the principle and practical application of PLC in the field of electrical automation engineering, and optimises the automatic control system based on PLC.