# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 2 Related Works
The method first extracts the features of the audio that need to be trained, performs fuzzy C-means clustering on these features, and finally trains and detects through a plurality of hypersphere single-class SVM. Experiments show that the method is suitable for steganalysis of unknown steganography methods, and it also improves the accuracy of steganalysis compared with other clustering methods. The literature [14; 15] also propose detection method based on SVM for steganography from different angles, they also improve a lot in the detection rate. [PERSON] et al. [2] studied the significant changes caused by QIM steganography in G.729. It was found that the steganography would change the quantization index of the linear predictive coding (LPC) filter in the code stream. The statistical model is designed to realize the most feature extraction of the distribution characteristics of codewords. Combined with SVM, an integrated classifier system for steganographic detection is constructed to realize the fast and effective detection. Another study analyses that QIM steganography changes the distribution characteristics of phoneme in compressed speech streams. It constructs a phoneme vector space model and a phoneme state transition model, quantifies the phoneme distribution characteristics, and builds the steganographic detector [3; 16] using SVM. The method has high detection accuracy. It also can extract features in the compressed domain without decoding. It saves time for decoding speech. According to the changes of the characteristics after steganography, different feature selection may have different detection effects. [PERSON] et al. [17] found the pitch modulation steganography will change the correlation characteristics of the adjacent speech frame adaptive codebook in the compressed speech stream. In order to quantify the correlation characteristics, the codebook correlation network model is designed. Finally, a steganographic detector is constructed based on the obtained feature vector and SVM, it realizes fast and effective detection of pitch modulation information hiding. In a word, the steganography method based on QIM has the advantages of simple implementation, low algorithm complexity, small speech distortion, good concealment and robustness, etc., which is more and more favored by researchers. Steganalysis methods have emerged one after another, but the research progress is still relatively backward and needs to be continuously improved. Based on the above research background, this paper analyses the long-term characteristics of index distribution and the correlation between indexes. The contributions and novelty of this study are as follows. A method of speech steganalysis in G.723.1 based on QIM is proposed to improve the accuracy and reliability of the steganalysis algorithm. The algorithm of this paper mainly focuses on the steganalysis of the CNV-QIM method with good steganography effect. After constructing the classifier with PCA dimensionality reduction and SVM, the information steganography of different durations and different embedding rates is analysed. A first-class detection (ACD) and second-class detection (SCD) are proposed to prove the efficiency of the steganalysis. Finally, the accuracy and reliability are improved.