Open AccessSecuring audio transmission based on encoding and steganography
Author(s) -
Enas Wahab Abood,
Zaid Ameen Abduljabbar,
Mustafa A. Al Sibahee,
Mohammed Abdulridha Hussain,
Zaid Alaa Hussien
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v22.i3.pp1777-1786
Subject(s) - steganography , least significant bit , computer science , cover (algebra) , peak signal to noise ratio , transformation (genetics) , metric (unit) , wavelet , audio signal , wavelet transform , artificial intelligence , signal (programming language) , speech recognition , computer vision , embedding , image (mathematics) , speech coding , engineering , mechanical engineering , biochemistry , chemistry , operations management , gene , programming language , operating system
One of the things that must be considered when establishing a data exchange connection is to make that communication confidential and hide the file’s features when the snoopers intercept it. In this work, transformation (encoding) and steganography techniques are invested to produce an efficient system to secure communication for an audio signal by producing an efficient method to transform the signal into a red–green–blue (RGB) image. Subsequently, this image is hidden in a cover audio file by using the least significant bit (LSB) method in the spatial and transform domains using discrete wavelet transform. The audio files of the message and the cover are in *.wav format. The experimental results showed the success of the transformation in concealing audio secret messages, as well the remarkability of the stego signal quality in both techniques. A peak signal-to-noise ratio peak signal-to-noise ratio (PSNR) scored (20-26) dB with wavelet and (81-112) dB with LSB for cover file size 4.96 MB and structural similarity index metric structural similarity index metric (SSIM) has been used to measure the signal quality which gave 1 with LSB while wavelet was (0.9-1), which is satisfactory in all experimented signals with low time consumption. This work also used these metrics to compare the implementation of LSB and WAV.