Wavelet Transform Based Steganography Technique to Hide Audio Signals in Image

Abstract

Information security is one of the most important factors to be considered when secret information has to be communicated between two parties. Cryptography and steganography are the two techniques used for this purpose. Cryptography scrambles the information, but it reveals the existence of the information. Steganography hides the actual existence of the information so that anyone else other than the sender and the recipient cannot recognize the transmission. In steganography the secret information to be communicated is hidden in some other carrier in such a way that the secret information is invisible. In this paper an image steganography technique is proposed to hide audio signal in image in the transform domain using wavelet transform. The audio signal in any format (MP3 or WAV or any other type) is encrypted and carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analyzed for different attacks. It is found that the technique is robust and it can withstand the attacks. The quality of the stego image is measured by Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), Universal Image Quality Index (UIQI). The quality of extracted secret audio signal is measured by Signal to Noise Ratio (SNR), Squared Pearson Correlation Coefficient (SPCC). The results show good values for these metrics.

Audio Data Hiding Based on Sample Value Modification Using Modulus Function

“…This is because the distortion that is done on the stego file is not that much since not all the samples are modified. Second, we objectively measure the stage quality by comparing it with the original file by using the following measurement methods: (i) SNR [11] that uses the square value of each sample to demonstrate the difference between the original audio signal and the stego audio signal as presented in Eq. (5), where is the number of the samples, is the original cover audio samples, and is the stego audio samples.…”

“…The former directly uses and handles the value of the audio samples and directly embed the data such as LSB methods and difference expansion; while the latter, which is called frequency domain, converts the audio into another domain and then embeds the secret bits within that particular domain such as discrete wavelet transform (DWT). In [10,11], The obtained values from this DWT are used to hide the secret binary bits.…”

Hiding Voice Message using Both Cryptography and Steganography

“…are common attacks that the stego-image may experience. [9] 4) Capacity: it means the amount of data that a steganography algorithm can effectively mask without causing visual impairment to the image within a chosen cover medium. The rate of embedding is expressed mostly in absolute measurement (like the size of the secret message).…”

An Image Steganography using Particle Swarm Optimization and Transform Domain

“…Message bit is embedded in LSB of DWT coefficients of the image [10]. Steganographic approach to hide audio signal within the image using DWT proves to provide increased security [11]. The image is segmented region wise and is combined with PSO which results in SRG (Seeded Region Growing).…”