## 3 Experimental Analysis  
Digital image steganography is very popular for covert communication. Steganalysis is challenging as there are little changes with a lower impact on the stego image. In this paper, four popular content-adaptive steganography schemes are evaluated by classifying cover and stego images. HILL, Mi-POD, S-UNIWARD, and WOW steganography schemes are tested using the proposed scheme on BOWS2 [35] and BOSSBase [36] image sets. Each set has ten thousand images with a dimension of 512 \(\times\) 512 pixels of different varieties. Experimental analysis is performed after changing the size of the image to 256 \(\times\) 256 pixels using interpolation. In most of the previous schemes, 256 \(\times\) 256 pixel images are considered. The proposed scheme is compared with the latest schemes, SRNet [28], Ye-Net [27], Yedroudj-Net [30], and Zhu-Net [32]. BOSSBase image set is used to create cover and stego images for experimental analysis and experimental results are shown in Tables 1-6. Five thousand images of each class, stego and cover, are considered for training the CNN. One thousand and four thousand images are considered for validation and testing, respectively. In Tables 7 and 8, experimental results are shown using BOWS2 [35] and BOSSBase [36] image sets. The results are defined in terms of detection error. The small value of detection error represents a better performance. the detection accuracy. The improvement in experimental results can be seen in four steganography schemes with three payloads. In general, one preprocessing or non-trainable convolutional layer is considered in previous networks. In the proposed scheme, CNN is guided by two non-trainable convolutional layers that contain thirty-one kernels. The first non-trainable layer is considered after the image input layer, and the second non-trainable layer after three trainable convolutional layers. In experiments, three non-trainable layers are also considered. However, the best performance is achieved when two non-trainable layers are used. There is a noticeable reduction in detection error when compared with the single non-trainable layer, as depicted in Table 2.  
The layer-specific learning rate is considered in the proposed scheme. Typically, a common learning rate is considered in each convolutional layer. Although the customized learning rate reduces the detection error moderately as shown in Table 3, customized learning rates are found after an exhaustive experimental analysis. Four steganography methods with three different payloads can be more effective in the proposed steganalysis scheme. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Steganography Scheme** & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**Mi-POD**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \hline Payload (bpp) & 0.2 & 0.3 & 0.4 & 0.2 & 0.3 & 0.4 & 0.2 & 0.3 & 0.4 & 0.2 & 0.3 & 0.4 \\ \hline \
\begin{tabular}{c} Thirty \\ Non-trainable \\ kernels \\ \end{tabular} & 0.3875 & 0.3242 & 0.2712 & 0.3475 & 0.2583 & 0.2378 & 0.3308 & 0.2166 & 0.1702 & 0.2523 & 0.1888 & 0.1512 \\ \hline
\begin{tabular}{c} Thirty-one \\ Non-trainable \\ kernels \\ \end{tabular} & 0.3796 & 0.3198 & 0.2661 & 0.3414 & 0.2548 & 0.2343 & 0.3234 & 0.2132 & 0.1682 & 0.2491 & 0.1857 & 0.1484 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection errors using thirty and thirty-one kernels.