Deep Audio Steganalysis in Time Domain

Deep Audio Steganalysis in Time Domain

연구 분야: Strategies

학회: IH&MMSec '20: Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security

Digital audio, as well as image, is one of the most popular media for information hiding. However, even the state-of-the-art deep learning model still has a limitation for detecting basic LSB steganography algorithms that hide secret messages in time domain of WAV audio. To advance audio steganalysis based on deep learning, deep audio steganalysis, in time domain of lossless audio format, we have developed a convolutional neural network that incorporates bit-plane separation, weight-standardized convolution, and channel attention. Training through payload curriculum learning and testing for six steganography methods demonstrated that our proposed model is superior to the other two deep learning models, achieving state-of-the-art performance. We expect our approach will provide insights to create a breakthrough for deep audio steganalysis.