## 3 Experimental Analysis
\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 ReLU & 0.3856 & 0.3236 & 0.2720 & 0.3462 & 0.2581 & 0.2397 & 0.3302 & 0.2170 & 0.1721 & 0.2540 & 0.1900 & 0.1519 \\ \hline Cipped ReLU & 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 4: Detection errors using ReLU and clipped ReLU. \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 Softmax Classifier & 0.3854 & 0.3262 & 0.2735 & 0.3476 & 0.2620 & 0.2422 & 0.3298 & 0.2202 & 0.1757 & 0.2553 & 0.1933 & 0.1567 \\ \hline SVM Classifier & 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 5: Detection errors using softmax and SVM classifiers. In Table 7, both BOWS2 and BOSSBase image sets are considered for experimental analysis. Seven thousand images from each dataset are considered as the cover image for training the CNN. Corresponding stego images are created using a particular steganography scheme with specific payloads. The trained CNN is evaluated using six thousand remaining images of BOWS2 and BOSSBase image sets. There is a significant reduction in detection error after considering twenty-eight thousand images of both classes for training and twelve thousand images for testing the proposed CNN. Moreover, the experimental results show that the categorization error increases as the payload increases, which implies that a low payload makes the classification difficult. In Table 8, experimental results are displayed after augmenting BOWS2 and BOSSBase image sets. One hundred thousand images are considered for training the CNN, and twenty-five thousand images for testing the trained CNN. There is a tremendous reduction in detection error using augmented images. Then, ten thousand images (Table 6) and twenty-eight thousand images (Table 7) are considered for training the CNN. The proposed scheme outperforms the other schemes and Zhu-Net by decreasing the detection error from 4% to 10%. Thirty high-pass kernels were applied by [PERSON] et al. [27], [PERSON] et al. [30], and [PERSON] et al. [32]. The proposed scheme outcomes have been improved significantly with the use of thirty-one kernels and two non-trainable convolutional layers. \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 SRNet & 0.4560 & 0.3789 & 0.3305 & 0.4221 & 0.3417 & 0.2806 & 0.3568 & 0.2686 & 0.2225 & 0.2850 & 0.2226 & 0.1783 \\ \hline Ye-Net & 0.4672 & 0.4202 & 0.3736 & 0.4311 & 0.3733 & 0.3470 & 0.4058 & 0.3301 & 0.2706 & 0.3228 & 0.2922 & 0.2214 \\ \hline Yedroudj-Net & 0.4710 & 0.4216 & 0.3372 & 0.4294 & 0.3798 & 0.2952 & 0.4122 & 0.3189 & 0.2757 & 0.3074 & 0.2652 & 0.2071 \\ \hline Zhu-Net & 0.3888 & 0.3339 & 0.2878 & 0.3385 & 0.2828 & 0.2576 & 0.3167 & 0.2391 & 0.1951 & 0.2689 & 0.2339 & 0.1489 \\ \hline Proposed Scheme & 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 6: Detection errors of the proposed and other schemes using BOSSBase image set.