The weakness of Human Auditory System (HAS) led the audio steganography process to be used in hiding data in the digital sound. Audio steganography is implemented here by using Least Significant Bit (LSB) algorithm to hide message into multiple audio files. This is achieved by 1 st, 2 nd, 3 rd, and 4 th bits hiding ratios. In comparison to other used bits, hiding results show that the use of 1 st bit in LSB method for embedding data is much better than those used bits as expected. In addition to that and according to the results, file’s size affects strongly upon the effectiveness of the embedding process while hiding starting position doesn’t affect upon the variation of the adopted statistical estimators regardless to which bit is used. Among the statistical estimators that have been adopted here, the Mean Absolute Error (MAE) seems to be the best one in testing hiding process.
The process of embedding secret messages into digital sound is known as audio steganography [
Embedding techniques are chosen according to requirement. Some of these are LSB coding, parity coding, spread spectrum phase coding and echo hiding [
Because of its highest capacity for data and the easiest way to implement in comparing with the other techniques, Least Significant Bit (LSB) method has been adopted in this research.
Least Significant Bit (LSB) technique is one of the simplest approach for secure data transfer. In this technique, LSB of binary sequences of each sample of digitized audio file is replaced with the binary equivalent of the secret message [
To hide the letter “D” as an example which has the ASCII code equal to 68 that is 01000100 inside eight bytes of a cover, the process of LSB can be shown as follow [
Among different approaches to hide a secret message inside an audio file, LSB coding method is proposed. This can be achieved by replacing the first, second, third and fourth bit of the audio file (.WAV format) respectively with its equivalent bit in the binary message. This process begins from the starting hiding position which is only known by the encrypted and recipient persons. Hiding results have been examined through some statistical estimators [
I. Signal-to-Noise Ratio (SNR)
It is used as a measure of quality reconstruction of the audio file, given by
where r(i), t(i) are the values of the ith samples in the original and stego audio file, respectively, n is the audio file’s length.
II. Peak Signal-to-Noise Ratio (PSNR)
The PSNR is the ratio between maximum possible power and corrupting noise that affect the representation of the audio file. In this case, the signal is the original audio file and the noise is the produced error for the embedding process. The PSNR is given by;
The high values of SNR and PSNR indicates the high security, because they indicate the minimum difference between the original and the stego values.
III. Root Mean Square Error (RMSE)
It is used to quantify the difference between values implied by the original and the stego files. The RMSE is defined as;
IV. Mean Absolute Error (MAE)
It is the difference between the original and stego values. So, no one can suspect the presence of any hidden information. The MAE is given by;
The low values of RMSE and MAE indicate the high security, because they include the minimum difference between the original and new audio files.
The algorithm for LSB has been successfully tested through embedding a message into multiple wave audio files of various sizes which can be summarized in
Histograms for the audio wave file before and after coding are shown in
The symmetric with periodic behaviors for the difference between the two histograms can be seen clearly for the case of using 1st bit, while it isn’t the case for 4th bit in LSB.
For the first audio file, the process of hiding a secret message inside different positions of the audio wave file has been executed. Results show that starting hiding position doesn’t affect upon the variation of the statistical estimators regardless to which bit is used. This can be seen clearly from
In all figures, a similar behavior with equally spaced curves can be seen obviously for all SNR and PSNR variations. On the other hand, an increasing gap has been noticed between MAE and RMSE variation curves. In addition to the previous notice and according to its lowest value, MAE seems to be the best statistical estimator in testing hiding process.
File’s size | Audio’s file name |
---|---|
3338 | Test1 |
44408 | Test2 |
23992 | Test3 |
21816 | Test4 |
Without any fear of eavesdropper, a new-audio file having a message hidden into it can be sent successfully by using different ways of LSB technique (i.e. 1st, 2nd, 3rd & 4th bit respectively). Regardless to which bit is used, starting hiding position doesn’t affect upon the statistical estimators in their variation. Results show that MAE can be used as a best estimator in testing hiding process. After all one can ensure that 1st bit in LSB technique is better than other used bits in hiding process.