Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords

Abstract

With the wide application of adaptive multi-rate (AMR) speech coder, steganography and steganalysis based on AMR coded speech streams have become a hot spot in the field of information hiding. Quantization Index Modulation (QIM)-based steganography is one of the most effective approaches to hide secret information into AMR coded speech streams with excellent imperceptibility and robustness. So far, accurate detection for QIM-based steganography in short-length or low-embedding-rate speech streams remains an open problem, though some approaches can complete detection in a short time. To address this challenge, we first analyze and verify the characteristics of QIM-based steganography, and present a novel and high time-efficient steganalysis model based on distributed representations. Specifically, a codeword embedding layer is introduced to capture distributed representations with a denser space; then we introduce a bidirectional Long Short-Term Memory (LSTM) layer and propose a gated attention mechanism to provide contextual distribution features with better generalization capabilities; finally, a Multi-Layer Perceptron (MLP) classifier is designed to distinguish normal or steganographic objects. The experimental results demonstrate that the proposed model can effectively detect QIM-based steganography in AMR speech streams and outperform the state-of-the-art ones.