Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography–An Innovative Approach

Abstract

We propose a novel LSB-BMSE method that enhances LSB audio steganography. It uses an innovative mechanism, Binaries of Message Size Encoding (BMSE), to embed a secret message after hiding its size in random samples. First, the secret message is compressed using Huffman coding, then encrypted by AES-128. The audio cover is split into number of blocks depending on secret message size. A secure key BMSE , output from the BMSE mechanism, is used to embed the secret message in random blocks and bytes adaptively according to its size. It is implemented using MATLAB and standard parameters: Perceptual Evaluation of Speech Quality and NIST Statistical Test Suite were used to measure imperceptibility between cover & stego audios and randomness of BMSE mechanism respectively. The fidelity was tested using Mean Square Error, Peak Signal to Noise Ratio and Signal-to-Noise Ratio. Comprehensive experiments on widely used metrics demonstrate that LSB-BMSE significantly surpasses existing methods in terms of hiding capacity and imperceptibility. Moreover, LSB-BMSE shows resistance to brute force attacks and statistical analysis. Although it was robust towards re-sampling attacks, nevertheless was not vigorous towards noise nor LSB attacks. The key BMSE complies with Kerckhoff's principle and exhibits avalanche criteria. The tested proposed LSB-BMSE proclaimed its effectiveness.

Ameliorating LSB Using Piecewise Linear Chaotic Map and One-Time Pad for Superlative Capacity, Imperceptibility and Secure Audio Steganography

“…Hence, its structural limitations necessitate an improvement to the traditional approach making it less predictable while preserving its high capacity. Researchers [4,34,35,38] are just few examples of enhancements to LSB audio steganography. Unquestionability, these improvements to the traditional LSB are much more secure as they are more resistant to steganalysis attacks and many improved the capacity and imperceptibility.…”

“…Nonetheless, its deterministic embedding way provide attackers with intentional and unintentional attacks opportunities that may damage the data. Ergo, it is very sensitive towards noise attacks, re-sampling, LSB attacks, amplification, compression, et cetera [4].…”

“…Hiding the existence of a secret message is as vital as disguising its content, ergo steganography came into light. Specifically, audio steganography is the art and science of hiding any secret data like text messages and binary files into any audio files viz WAV, MIDI, AVI, MPEG and MP3 files [1][2][3][4]. The selection of audio media was on account of that it has the essential quality of representing the amplitudes in real number format, hence producing miniature distortions after embedding secret messages.…”

“…Four main facets are assessed in data hiding, namely hiding capacity, imperceptibility, security, and robustness [3][4][5].…”

Identifying optimal message embedding location in audio steganography using generative adversarial networks

“…Payload localization in pseudorandom scrambled JPEG image steganography has recently been established using co-frequency subimage filtering [19]. The fidelity of the predicted cover images affects the precision of this payload locating approach, which can be enhanced by developing a more accurate estimator [20].…”