Multi-Agent Deep Learning for the Detection of Multiple Speech Steganography Methods
Congcong Sun, Hui Tian, Peng Tian, Haizhou Li, Zhenxing Qian
Abstract
The ability to detect multiple steganographic methods in speech streams is an important prerequisite for steganalysis methods to move from theory to practical application, but it is also a challenging problem. To address this challenge, we propose a novel steganalysis method based on multi-agent deep learning, which can effectively detect multiple steganography methods in speech streams. Our method utilizes multiple agents to learn the features of multiple sub-training datasets separately and then fuses the information of each agent through the weight parameter aggregation mechanism to obtain the final weight parameter of the steganalysis model. Experimental results show that our proposed method outperforms the state-of-art steganalysis methods. In particular, for low embedding rates, the presented method increases average detection accuracy by about 9%.