SANet: A Compressed Speech Encoder and Steganography Algorithm Independent Steganalysis Deep Neural Network

Most of the existing steganalysis methods for low-bit-rate compressed speech are specifically designed for a particular speech encoder or category of steganography methods, limiting their generalization capability. These methods require pre-selection of codewords affected by the specific steganographic process as input to the steganalysis models. In order to overcome this limitation and enhance the practicality of steganalysis algorithms, we propose a compressed S peech encoder and steganography A lgorithm independent steganalysis N etwork, named SANet . Irrespective of the specific steganography algorithm used, modifications to the codewords will impact the sequential correlation characteristics of uncompressed domain (time domain) speech. Additionally, the compressed speech streams from different coders are unified in the uncompressed domain format. Therefore, this article introduces an intermediate representation based on the uncompressed domain and develops a neural network that utilizes collaborative correlation features to extract steganography-sensitive characteristics from this representation. Experimental results demonstrate that our proposed method achieves state-of-the-art detection performance for various steganography algorithms under different speech encoders.