## 3 Experimental Analysis
\begin{table}
\begin{tabular}{c 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} Single \\ Non-trainable \\ Layer \\ \end{tabular} & 0.3925 & 0.3287 & 0.2739 & 0.3489 & 0.2624 & 0.2420 & 0.3337 & 0.2217 & 0.1749 & 0.2545 & 0.1905 & 0.1521 \\ \hline \
\begin{tabular}{c} Two \\ Non-trainable \\ Layers \\ \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
\begin{tabular}{c} Three \\ Non-trainable \\ Layers \\ \end{tabular} & 0.3841 & 0.3246 & 0.2696 & 0.3479 & 0.2578 & 0.2390 & 0.3289 & 0.2157 & 0.1707 & 0.2525 & 0.1894 & 0.1503 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection errors using different non-trainable convolutional layers. \begin{table}
\begin{tabular}{c 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} Fixed \\ learning rate \\ \end{tabular} & 0.3898 & 0.3278 & 0.2715 & 0.3506 & 0.2606 & 0.2388 & 0.3289 & 0.2164 & 0.1731 & 0.2548 & 0.1898 & 0.1516 \\ \hline
\begin{tabular}{c} Layer-specific \\ learning rate \\ \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 3: Detection errors of fixed and layer-specific learning rate. In Table 4, experimental results are compared using ReLU and clipped ReLU. High-pass kernels in non-trainable layers increase the values, although CReLU is considered to control the increase. As discussed in the previous section, some specific values are controlled by the threshold in clipped ReLU. Similar to ReLU, CReLU replaces the negative components with zero while replacing the non-negative items that are higher than a threshold value with a particular threshold value. Positive elements below the threshold value are retained. Support vector machine (SVM) is a very popular classifier. The proposed scheme replaces the softmax classifier with an SVM classifier with a quadratic kernel for a better detection of cover and stego images. There is a fair deduction in detection errors using the SVM classifier as shown in Table 5. A detailed analysis regarding the outperformance of SVM over softmax classifier is performed by [PERSON] [43]. While SVM seeks to maximize the margin between data points belonging to different classes, the softmax classifier minimizes the cross-entropy. The proposed steganography scheme can successfully classify the cover and four steganography techniques with 0.2, 0.3, and 0.4 bpp payloads. The proposed scheme is compared with the recent schemes SRNet [28], Ye-Net [27], Yedroudj-Net [30], and Zhu-Net [32] as shown in Table 6. The proposed scheme outperforms other schemes with the exception of two cases. As represented in Figure 2, the spread of stego noise is more uniform than other steganography schemes, making the detection of HILL very challenging. HILL has the maximum detection error than the other schemes. WOW has the least detection error as the stego noise is more concentrated in some specific areas. The proposed scheme outperforms other schemes with a payload of 0.2 bpp, with the exception of Mi-POD and S-UNIWARD. A detection error reduction from 2.37% to 13.77% is observed in most cases when the proposed scheme is used, with an average decline of 6.92% in classification error.