## 2 The Proposed Scheme  
Numerous image steganography schemes are used to hide some secret data in an image. At present, deep networks are found to be effective in state-of-the-art steganalysis schemes. In this paper, a robust deep neural network for the steganalysis of content-adaptive steganography schemes is proposed. The variation in different steganography schemes can be understood from Figure 2, and only one standard solution can be proposed for steganalysis. The effect of HILL, Mi-POD, S-UNIWARD, and WOW steganography schemes can be understood by covariance plots of image entropy in Figure 3. A total of twenty thousand images from BOWS2 [35] and BOSSBase [36] datasets is considered as a cover image. The same number of corresponding stego images with payload PL = {0.1, 0.2, 0.3, 0.4} is created using HILL, Mi-POD, S-UNIWARD, and WOW steganography schemes. In Figure 3, it is evident that a very small gap exists for cover and HILL PL = 0.1, which indicates that the detection of HILL may be challenging. However, the gap is more evident in Mi-POD followed by S-UNIWARD and WOW. Moreover, the behavior of covariance plot is reflected in the classification of cover and stego images. The highest detection error is found in HILL, followed by Mi-POD, S-UNIWARD, and WOW. The block diagram of the proposed scheme is shown in Figure 4. The proposed convolutional neural network has two non-trainable convolutional layers. Thirty high-pass kernels and one neutral kernel of size 5 \(\times\) 5 are used in non-trainable convolutional layers. Figure 3: Covariance plots of cover and stego images entropy. In Figure 5, one neutral kernel and three high-pass kernels are displayed to understand the effect of kernels. Each kernel provides a different type of information. High-pass kernels increase the stego noise. Conventionally, in [27; 30; 31; 32; 33; 34], only one non-trainable convolutional layer was used with thirty high-pass kernels. The neutral kernel is applied to retain the unprocessed information from the preceding layer. Moreover, a neutral filter increases the statistical information as unprocessed information is added with high-pass kernel operated information. The effect is verified in experimental analysis. The proposed CNN is guided by two non-trainable convolutional layers. It has already been established that a single non-trainable convolutional layer with thirty high-pass kernels enhances the detection performance for cover and stego images. In experimental analysis, it is discovered that the performance can improve significantly using an additional non-trainable convolutional layer. However, there is an adverse effect when three non-trainable convolutional layers are tried. Therefore, two non-trainable layers are considered. The second non-trainable layer provides the best results when used not very far from the first non-trainable layer. If the distance between non-trainable layers becomes very far, then there is a drop in the performance of the network. Figure 4: Block diagram of the proposed scheme. Figure 5: Neutral kernel and high-pass kernels. The network considers ten trainable convolutional layers, and their weights are initialized using Glorot and Bengio [37] scheme. Each trainable convolutional layer is followed by batch normalization and a clipped ReLU layer [38]. The layer-specific learning rate is considered for each trainable convolutional layer. In previous research, a fixed learning rate, irrespective of the layer, was considered. Layer-specific learning rate [39] can improve the performance substantially which is found to be effective in many other applications [40; 41]. The layer-specific learning is adapted by rigorous experimental analysis. The ReLU layer is utilized in the majority of the earlier networks. ReLU replaces the negative elements with zero and remains intact in non-negative elements. The ReLU can be defined as:  
\[E(x)= \begin{cases}x,&x\geq 0\\ 0,&x<0\end{cases} \]  
In this paper, the clipped ReLU (CReLU) is used to give an improved control in the proposed CNN.