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Mathematics

Speech Steganography System Using Lifting Wavelet Transform

Profile image of Ahlam MajeadAhlam Majead

2016, Information (Koganei)

Abstract

This paper presents a new lossless speech steganography approach based on Integer-to-Integer Lifting Wavelet Transform (Int2Int LWT) and Least Significant Bits (LSBs) substitution. In order to increase the security level a simple encryption with chaotic key has been proposed. The proposed system has a high sensitivity in choosing keys because a small change in CKG causes a new secret key for transmitting. Speech steganography algorithm that based on (Int2IntLWT) can satisfy full recovery for the embedded secret messages in the receiver side.

Figures (11)
Figure (1) forward transforms operation for three levels of LWT a a aaa Llifting scheme i is introduced to fast DWT, this easily achieved by the computer due to the great reduction in calculations. LWT scheme usually requires less mathematical operations compared with traditional approach convolution. LWT achievement does not require additional memory because of the in-place calculation features of the lifting. This is particularly suitable for the devices implementation of with a limited memory. LWT scheme submitted integer to integer transformation appropriate for lossless processing signal 7! This approach is totally based on the spatial performance of the DWT. Basic concept of LWT is to exploit the correlation infrastructure that present in most real life signals to build a dispersed approximation. Correlation infrastructure is normally localization into space (time) and frequency; neighboring samples and from that are in distant from. Figure (1) represents the forward trans LWT with the three stages (split, production and update) © 4!: frequencies are much more interconnected form scheme of three levels of three levels
Figure (1) forward transforms operation for three levels of LWT a a aaa Llifting scheme i is introduced to fast DWT, this easily achieved by the computer due to the great reduction in calculations. LWT scheme usually requires less mathematical operations compared with traditional approach convolution. LWT achievement does not require additional memory because of the in-place calculation features of the lifting. This is particularly suitable for the devices implementation of with a limited memory. LWT scheme submitted integer to integer transformation appropriate for lossless processing signal 7! This approach is totally based on the spatial performance of the DWT. Basic concept of LWT is to exploit the correlation infrastructure that present in most real life signals to build a dispersed approximation. Correlation infrastructure is normally localization into space (time) and frequency; neighboring samples and from that are in distant from. Figure (1) represents the forward trans LWT with the three stages (split, production and update) © 4!: frequencies are much more interconnected form scheme of three levels of three levels
Figure (2) Block diagram for preprocessing stage The inputs are secret message and CKG1 generated from algorithm (1).
Figure (2) Block diagram for preprocessing stage The inputs are secret message and CKG1 generated from algorithm (1).
Figure (3) Block diagram for embedding stage Two chaotic keys (CKG1, CKG2) also were used to select the chaotic indexes of coefficients that their LSBs are replaced with message bits. XOR operation was performed between the block message and the chaotic numbers resulted from CKG1. The embedding stage algorithm is listed in algorithm (2): The speech signal is framed to the number of frames, each frame contain 512 samples. Int2IntLWT has been used to convert speech signal to the frequency domain. First and second levels of Int2IntLWT were implemented for each frame where the results were four sub bands matrixes. High frequency for sub-band1 and sub-band2 were used for embedding operation. The secret message data were scrambled by using first chaotic key generation (CKG1), and then the message was divided in a number of blocks bits. The block of the message was divided into two bits sets. The two sets were embedded within each frame of cover by replacing LSBs method for each coefficient of two high frequency sub-band. The embeeding stage is depicted in figure (3).
Figure (3) Block diagram for embedding stage Two chaotic keys (CKG1, CKG2) also were used to select the chaotic indexes of coefficients that their LSBs are replaced with message bits. XOR operation was performed between the block message and the chaotic numbers resulted from CKG1. The embedding stage algorithm is listed in algorithm (2): The speech signal is framed to the number of frames, each frame contain 512 samples. Int2IntLWT has been used to convert speech signal to the frequency domain. First and second levels of Int2IntLWT were implemented for each frame where the results were four sub bands matrixes. High frequency for sub-band1 and sub-band2 were used for embedding operation. The secret message data were scrambled by using first chaotic key generation (CKG1), and then the message was divided in a number of blocks bits. The block of the message was divided into two bits sets. The two sets were embedded within each frame of cover by replacing LSBs method for each coefficient of two high frequency sub-band. The embeeding stage is depicted in figure (3).
Figure (4) shows the steps of extraction stage. It is implemented as the same way of embedding stage but in reverse form. Figure (4) Block diagram for extraction phase
Figure (4) shows the steps of extraction stage. It is implemented as the same way of embedding stage but in reverse form. Figure (4) Block diagram for extraction phase
Figure (6) Histogram of components (RGB) (a) original image (b) scrambled image(c) Extracted image
Figure (6) Histogram of components (RGB) (a) original image (b) scrambled image(c) Extracted image
e Text message with length 15206 character. Figures (6, 7 and 8) show the three type of message in three cases of original, encrypted and extracted message. Tables (1, 2 and 3) illustrate the objective quality measurements using four files of speech covers. The tables pointing that the quality measurements signal to noise ratio (SNR), signal to noise ratio segmental (SNRs, ) and signal to noise ratio spectral(SNRspe-)) are decreasing with increasing the replaced LSBs numbers. The measure MSE increased with increasing the replaced LSBs numbers, because of increasing error in host signal. Finally the parameter runtime and correlation test (Ryy ) are decreasing with increasing the replaced LSBs numbers. Figure (5) Waveform for the original and stego speech signal
e Text message with length 15206 character. Figures (6, 7 and 8) show the three type of message in three cases of original, encrypted and extracted message. Tables (1, 2 and 3) illustrate the objective quality measurements using four files of speech covers. The tables pointing that the quality measurements signal to noise ratio (SNR), signal to noise ratio segmental (SNRs, ) and signal to noise ratio spectral(SNRspe-)) are decreasing with increasing the replaced LSBs numbers. The measure MSE increased with increasing the replaced LSBs numbers, because of increasing error in host signal. Finally the parameter runtime and correlation test (Ryy ) are decreasing with increasing the replaced LSBs numbers. Figure (5) Waveform for the original and stego speech signal
Figure (7) the waveform of secret speech message before and after embedding operation.
Figure (7) the waveform of secret speech message before and after embedding operation.
It is Possible to hide more than one secret message inside the same speech cover under the condition of suitable size cover with the required number messages that hiding in it. It is possible to use speech signal as channel to send any secret message between two persons for the purpose of secret communication with good security. Table (2) objective measurements of hiding speech message (1 minute) within speech file (4 minutes)
It is Possible to hide more than one secret message inside the same speech cover under the condition of suitable size cover with the required number messages that hiding in it. It is possible to use speech signal as channel to send any secret message between two persons for the purpose of secret communication with good security. Table (2) objective measurements of hiding speech message (1 minute) within speech file (4 minutes)

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References (8)

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  7. Eman Hato Hashim , "Speech Signal Encryption Using Chaotic Maps", M.Sc. Thesis, Al Mustansiriyah University, Computer Science department, September 2013.
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