Abstract 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 streaming 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 sub-networks, 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.

中文翻译：

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检测低比特率 VoIP 语音流的异构并行隐写术

摘要 本文考虑了检测流媒体上的异构并行隐写术 (HPS) 的新任务。该任务是通过多种正交隐写方法检测隐藏在流媒体帧中的机密信息的存在。我们的目标是在这项工作中检测 HPS，用于低比特率的互联网协议语音 (VoIP) 语音流，这是一种广泛使用的流媒体。具体地，利用两种隐写方法，即量化索引调制和音调调制隐写术来形成HPS。在低比特率 VoIP 语音流上检测 HPS 对现有的隐写分析方法具有挑战性。为了实现目标，我们提出了一种名为隐写分析特征融合网络（SFFN）的新型深度模型。SFFN由三个子网络组成，即一个特征学习网络，一个特征融合网络和一个分类网络。通过三个子网络，SFFN 可以有效地为 HPS 中使用的隐写方法提取隐写分析特征，并可以融合这些特征以做出可信的预测。实验结果表明，我们的方法在检测 HPS 时优于最先进的隐写分析方法。此外，我们的方法满足实时检测的要求。