Audio Stegging using Evolutionary Algorithms

Abstract

Abstract Three security approaches, namely cryptography, watermarking, and steganography, are required due to the rapidly expanding digital communication over the internet. With cryptography, the message’s content is changed, turning plain text into cipher text, but in watermarking, data is hidden to carry additional information like ownership and copyright. However, these methods allow for the detection of secret messages, which increases the intrusive party’s interest in obtaining the secret message while it is being transmitted. The system’s data should always meet the three criteria of availability, confidentiality, and integrity. Steganography has thus emerged as the most trustworthy security method today for preserving the confidentiality and privacy of data while it is being sent. Steganography is the art and science of hiding secret information within a carrier medium, such as images or audio files, in order to protect the confidentiality and integrity of the information. The goal of steganography is to prevent the attacker from knowing what the actual data is. This is used to stop data from being shared with unauthorized system users. To covertly incorporate vast amounts of hidden information into an image while ensuring that others are unaware of its presence, an image steganography algorithm is needed. Image steganography is the practice of concealing data which could be text, images, or audio files within an image. In recent years, various techniques, including the Least Significant Bit (LSB) approach, Bit Plane, Spiral Embedding, Metadata Manipulation, and others, have been proposed for implementing steganography. However, the security of the hidden information can be enhanced by combining steganography with cryptography. This research proposes a new steganography technique that aims to hide audio files within an image using different metaheuristic algorithms, such as Ant Lion Optimizer (ALO), Harris Hawks Optimization (HHO), Firefly Optimization (FO), Cuckoo Search (CS), and Moth Flame Optimization (MFO). These algorithms are chosen for their ability to find optimal solutions in large search spaces. The proposed method builds on the LSB technique by partitioning the carrier image into blocks and applying the LSB technique to each block to hide the audio data. To increase the security of the hidden data, a metaheuristic algorithm is used to generate a random sequence of locations within the carrier image where the audio bit stream will be embedded. This random sequence generation makes it difficult for an attacker to locate and extract the hidden data. The proposed method aims to address some of the challenges faced by existing image steganography techniques, such as low capacity and weak security. The performance of the proposed method will be evaluated using various metrics, including Capacity, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Square Error (MSE). The experimental results are expected to demonstrate the effectiveness of the proposed method in terms of improved capacity and increased security, contributing to the field of steganography. Overall, this research contributes to the field of steganography by proposing a new and improved technique that combines metaheuristic algorithms with LSB embedding for secure audio steganography in images.