# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 2 The Proposed Scheme
Ten thousand images of BOWS2 were considered after resizing to dimensions of 256 \(\times\) 256 pixels to plot the entropy covariance plot. In Figure 5a, the plot is displayed for the cover and HILL stego-images of with various payloads of 0.1 bpp, 0.2 bpp 0.3 bpp, and 0.4 bpp. Entropy covariance plots of S-UNIWARD and WOW algorithms are displayed in Figure 5b,c, respectively. The model of plots is alike for the S-UNIWARD, WOW, and HILL steganography techniques. However, the gap between the cover and stego-images varied according to the payload. Thus, a common convolutional neural network can be suggested to identify between the cover and HILL, S-UNIWARD, and WOW different stego-images. Figure 5: _Cont._  
The proposed convolutional neural network block diagram is displayed in Figure 6 after considering the previous literature and the above discussion. In most of the previous CNN based techniques [27; 30; 31; 32; 33; 34], the SRM filters [11] were used as preprocessing layers in the CNN. In this paper, two preprocessing layers were used for revealing the stego-noise effectively. One high-boost kernel with dimensions of 3 \(\times\) 3 was considered in the first non-trainable preprocessing layer to capture the stego-noise. In the second non-trainable preprocessing layer, thirty SRM filters with dimensions of 5 \(\times\) 5 were taken. Furthermore, the bottleneck approach applied in each convolutional layer block as in [32] gave better results than the conventional approach. The abstract diagram of the bottleneck approach is displayed in Figure 7. In blocks 1-14, either 18 kernels or nine kernels were used. Unlike the previous CNN, the number of kernels was a lot less. The lower number of kernels reduced the computation cost drastically. In the experimental analysis, some experiments were performed by using a higher number of kernels but there was no advantage in the detection accuracy. Therefore, a fewer number of kernels were considered. Weight was initialized using the Glorot and Bengio [38] method. In every fourteen blocks, the order of the layer was as follows: convolution layer with 3 \(\times\) 3 filter, convolution layer with 1 \(\times\) 1 filter, bath normalization layer, and clipped rectifier linear unit layer [39]. Two skip connections were also used after block 5 and block 10 for better detection. No pooling layer was used until block 14 in the network. One global average pooling layer [30; 40] was considered after fourteen blocks. The consideration of only one GAP layer ensures the maximum flow of statistical information between the blocks. The RMSprop optimizer was utilized in CNN training. The number of epochs was considered as a hundred with a mini-batch size of twenty. Furthermore, features were extracted from the GAP layer using the activation function. The features of both classes (i.e., cover and stego-images) were classified using an SVM classifier with a Gaussian kernel as the function. Sequential minimal optimization [41] was used for fast and optimized convergence. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Payload (bpp)** & **HILL** & **S-UNIWARD** & **WOW** \\ \hline
0.2 & 45.43 & 40.58 & 53.33 \\ \hline
0.3 & 46.95 & 39.87 & 53.79 \\ \hline
0.4 & 48.61 & 41.27 & 54.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage increase of the pixels modified after high-boost filtering. Figure 5: Entropy covariance plots of the cover and stego-images. (**a**) Covariance plot of the cover and HILL. (**b**) Covariance plot of the cover and S-UNIWARD. (**c**) Covariance plot of the cover and WOW. In most of the earlier networks, the ReLU layer was utilized for thresholding. The negative elements were replaced with zero in the conventional ReLU layer. The ReLU can be better understood by the following function:  
\[R(i)= \begin{cases}i,&i\geq 0\\ 0,&i<0\end{cases}