The goal of steganalysis is to unearth a concealed message hidden inside an innocent carrier by steganography. Steganography in the hands of unlawful people can pose a great threat to society. Secret message is embedded as a noise (residue) which silently disrupts the statistical nature of the carrier without visual transformation. For the purpose of unearthing covert communication, modeling this noise (residue) is a crucial factor. In this paper, a hybrid deep learning framework is proposed with convolutional neural network (CNN) to emphasize the need for residual based investigation for digital image steganalysis. In order to model the noise residuals, five types of handcrafted features are extracted, in which the first four types rely on acquiring noise residuals based on filtering by linear and nonlinear filters, and the fifth type relies on residual acquisition method based on Empirical Mode Decomposition (EMD). The extracted features are categorized using a robust CNN with a new parallel architecture that is capable of learning intricate details from input features to classify it as cover or stego. Mostly CNNs are trained with raw images, but in the proposed method, the 1-dimensional residual features are stacked into 2-dimensional grid and are then used to train the CNN. Three spatial content-adaptive and four spatial non-content-adaptive algorithms are used to evaluate the performance of the proposed architecture. The experimental results show the versatility and robustness of the proposed hybrid architecture towards these algorithms when compared to existing state-of-the-art steganalytic methods. A practical issue prevalent in deploying steganalysis in real-world is ‘mismatch scenario,’ and based on the experimentation done in this paper, the proposed architecture performs well under stego algorithm mismatch and payload mismatch scenarios.

隐写分析的目的是通过隐写术发掘隐藏在无辜载体内部的隐秘信息。非法人手中的隐写术可能对社会构成巨大威胁。秘密消息被嵌入为噪音（残留物），可无声地破坏载体的统计性质，而无需进行视觉转换。为了发掘秘密通信的目的，对此噪声（残留）建模是至关重要的因素。在本文中，提出了一种具有卷积神经网络（CNN）的混合深度学习框架，以强调需要基于残差的研究来进行数字图像隐写分析。为了对噪声残差进行建模，提取了五种类型的手工特征，其中前四种类型依赖于基于线性和非线性滤波器的滤波来获取噪声残差，第五类依赖于基于经验模态分解（EMD）的残差获取方法。使用具有新并行架构的健壮的CNN对提取的特征进行分类，该结构能够从输入特征中学习复杂的细节，并将其分类为掩护或隐秘。大多数情况下，使用原始图像训练CNN，但是在提出的方法中，将一维残差特征堆叠到二维网格中，然后用于训练CNN。三种空间内容自适应算法和四种空间非内容自适应算法用于评估所提出体系结构的性能。实验结果表明，与现有的最新隐写分析方法相比，提出的混合体系结构对这些算法的多功能性和鲁棒性。