Since the BOSS competition, in 2010, most steganalysis approaches use a
learning methodology involving two steps: feature extraction, such as the Rich
Models (RM), for the image representation, and use of the Ensemble Classifier
(EC) for the learning step. In 2015, Qian et al. have shown that the use of a
deep learning approach that jointly learns and computes the features, is very
promising for the steganalysis. In this paper, we follow-up the study of Qian
et al., and show that, due to intrinsic joint minimization, the results
obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural
Network (FNN), if well parameterized, surpass the conventional use of a RM with
an EC. First, numerous experiments were conducted in order to find the best "
shape " of the CNN. Second, experiments were carried out in the clairvoyant
scenario in order to compare the CNN and FNN to an RM with an EC. The results
show more than 16% reduction in the classification error with our CNN or FNN.
Third, experiments were also performed in a cover-source mismatch setting. The
results show that the CNN and FNN are naturally robust to the mismatch problem.
In Addition to the experiments, we provide discussions on the internal
mechanisms of a CNN, and weave links with some previously stated ideas, in
order to understand the impressive results we obtained.

本研究探讨了卷积神经网络和全连接神经网络在语音隐写分析中的应用，结果表明相较于传统的 Rich Models（RM）集成分类器（EC）方法，如果 CNN 和 FNN 被很好的参数化，其结果更胜一筹，演示了在 clairvoyant 场景下，CNN 和 FNN 相较于 RM 与 EC 的分类错误率降低 16％以上，并且在封面 - 源不匹配情况下表现自然而强大。