Detection of heterogeneous parallel steganography for low bit-rate VoIP speech streams

This paper considers a new task of detecting heterogeneous parallel steganography (HPS) on streaming media. This task is to detect the existence of the confidential messages hidden in the frames of streaming media with multiple kinds of orthogonal steganographic methods. We target on detecting HPS in this work for low bit-rate Voice over Internet Protocol (VoIP) speech streams, which is a widely-used stream-ing medium. Specifically, two steganographic methods, i.e., Quantization Index Modulation and Pitch Modulation Steganography, are utilized to form the HPS. Detecting HPS on low bit-rate VoIP speech streams is challenging for existing steganalysis methods. To accomplish the target, we propose a novel deep model named as Steganalysis Feature Fusion Network (SFFN). SFFN consists of three subnetworks, i.e., a feature learning network, a feature fusion network and a classification network. With the three sub-networks, SFFN can effectively extract steganalysis features for the steganographic methods used in HPS and can fuse the features to make credible prediction. The experimental results demonstrate that our method is superior to the state-of-the-art steganalysis methods when detecting HPS. Besides, our method meets the requirement of real-time detection. (c) 2020 Elsevier B.V. All rights reserved.