A Novel Quantum Audio Steganography–Steganalysis Approach Using LSFQ-Based Embedding and QKNN-Based Classifier

Abstract

In this paper, a steganography–steganalysis model for quantum audio signals is presented. This model consists of two separate parts of steganography and steganalysis. In the steganography part, to increase the imperceptibility and enhance the undetectability of the least significant bit method, first, the quantum host audio signal is divided into quantum frames of the specified length, and then, using the triangular number sequence, some samples of each frame are selected for the embedding operation. Finally, the embedding process is carried out within the least significant fractional qubit of the amplitude information of the selected samples. The steganalysis part also includes a quantum universal steganalyzer for detecting the embedding operations of the steganography part and a feature extraction module for calculating the power feature of quantum audio signal frames. The recognition operation is based on the quantum K-nearest neighbor algorithm and the Hamming distance criterion. All quantum circuits of the steganography and steganalysis parts of the proposed model were simulated and then tested and evaluated using different audio files. Detection accuracy of over 90% indicates the accuracy and efficiency of the proposed method in quantum audio steganography detection in the context of quantum communication networks.