Backward movement oriented shark smell optimization-based audio steganography using encryption and compression strategies

Abstract

Audio steganography is the process of hiding secret information such as images, text, or video into the audio file. Data hiding in audio signals has been utilized in diverse applications like secret communication, hiding of data that may affect the safety and security of personnel and governments. Most of the recently introduced audio steganography approaches are content non-adaptive that may suffer low embedding capability and poor security. This paper proposes the audio steganography model for secure audio transmission during communication based on the meta-heuristic concept. In the embedding phase, the optimized instant selection is made for embedding the secret message (image) into the input audio. Further, the secret message to be embedded is encrypted using the optimized 2D-Logistic Chaotic Map. Here, the development of optimal instant selection and the optimized 2D-Logistic Chaotic Map are performed by the improved Shark Smell Optimization (SSO) called Backward Movement oriented SSO (BM-SSO). Once the secret message is encrypted, the compression of the message is done by Modified Huffman Encoding (MHE). Finally, the extraction phase uses the optimized 2D-Logistic Chaotic Map-based decryption. The proposed audio steganography by optimized instant selection for audio embedding minimizes the distortion error between the stego audio and original audio in terms of Mean Squared Error (MSE), and optimized 2D-Logistic Chaotic Map-based encryption aims to maximize the entropy of cipher image. The performance of the developed model has been estimated extensively against attacks, and simulation results are presented to prove the robustness of the proposed algorithm.