Lightweight AAC Audio Steganalysis Model Based on ResNeXt

Traditional AAC (Advanced Audio Coding) audio steganalysis methods rely on manual feature extraction, which results in low detection accuracy and low efficiency. Nowadays, the new steganalysis model based on neural network is very attractive, but its scale is large and its detection accuracy needs further improvement. Aiming at the above problems, this paper proposes a lightweight AAC audio general steganalysis model based on ResNeXt network. Firstly, the residual signal of QMDCT (Quantized Modified Discrete Cosine Transform) coefficients is calculated through a fixed convolution layer composed of multiple sets of high-pass filters. Then, based on the original structure of ResNeXt network, two ResNeXt blocks are designed to form a residual learning module, by which the steganalysis features in the QMDCT coefficients are further extracted. Finally, the classification module consisting of the fully connected layer and the Softmax layer is designed to obtain the classification result. The experimental results show that the model detection accuracy can reach more than 94% under all relative embedding rates when it operates on both the steganography algorithm based on the small value area of the QMDCT coefficient and the steganography algorithm based on the Huffman code sign bit. For the algorithm based on Huffman codeword mapping, even with the relative embedding rate of 0.1, the detection accuracy of the model can reach 85.5%, which is obviously better than the existing steganalysis schemes. Compared with other steganalysis schemes based on neural network, the model in this paper has fewer parameters, and reduces the scale by more than 40%, which is more lightweight and more efficient.