# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 2 Related Works  
The information hiding method based on QIM [4] was first proposed by [PERSON] and [PERSON] in 2001. The embedded domain classification QIM belongs to the hidden algorithm of the transform domain. It has the characteristics of small embedding capacity, small distortion, easy implementation, and good real-time performance. It was first used in image digital watermarking, and then gradually extended to image, speech steganography and steganalysis. In terms of transforming domain, [PERSON] et al. [5] analysed the transform domain coefficient histogram of audio after QIM steganographic, constructed the feature vector using the distance of the larger value of the histogram, and established a feature matrix. Then they proposed a QIM blind detection algorithm. The experiment proves that the method is only simple QIM is effective, and the effect of dither modulation QIM (DM-QIM) algorithm is not obvious. [PERSON] et al. [6] observed the interference of QIM steganography on the neighbourhood correlation in the transform domain. They used the kernel density estimation method to detect the statistical variation of the probability density function, and estimated the parameter model. Then they used it for information detection. The steganalysis has a high detection rate, but the amount of calculation is large, and the missed detection rate is also high. [PERSON] et al. [7] proposed a wavelet domain audio steganalysis method based on PCA. The audio signal is decomposed by four-stage discrete wavelet transform, and then the 36 statistical moments of the histogramand the frequency domain histogram for both the audio signal and its wavelet subbands are calculated as features. They used PCA pre-processed statistical features and radial basis functions (RBF) as a classifier. The scheme not only effectively reduces the dimensionality of the feature vector, but also simplifies the design of the classifier, and maintains the detection performance. It can be used to detect the wavelet domain LSB embedding of the audio signal, the QIM method and the addition method (AM). [PERSON] et al. [8] proposed a general steganalysis method based on Fourier spectrum statistics and Mel cepstrum coefficients, derived from the second order derivative of the audio signal. Then he also designed a Steganalysis way based on wavelet transform spectrum and Mel Cepstrum. Because the speech is compressed and encoded, the distortion is serious, and the characteristics of the carrier speech cannot be effectively reflected. Therefore, the detection rate is not high, but the detection of the least significant bit steganography is good. In addition, the literature [9; 10] also propose a blind detection method for steganography of images and audio from different angles, which achieve better detection results. Support Vetor Machine (SVM) were proposed by [PERSON] and [PERSON] in 1995 and are widely used in the steganalysis because of their advantages as classifiers. Some researchers have improved the vector machine and achieved better steganalysis. [PERSON] et al. [11] proposed a steganalysis method based on the optimized feature weighted SVM. The feature weighted kernel function is used to construct the feature weighted SVM. It improves classification accuracy of the SVM classifier by optimizing the feature selection and weighting processing. It effectively reduces the time complexity of the algorithm and has an efficient steganalysis capability. [PERSON] et al. [12] proposed a steganalysis method based on improved SVM. This method improves the steganalysis speed and detection accuracy of images. Researchers have combined the steganalysis method with the SVM to improve the efficiency and accuracy. [PERSON] et al. proposed an audio steganalysis method based on fuzzy C-means clustering and single-class SVM [13], which further solved the limitations of some potentially unknown steganography methods.