# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 3 Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{S. No.} & \multirow{2}{*}{
\begin{tabular}{c} **Steganography Technique/** \\ **Payload (bpp)** \\ \end{tabular} } & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \cline{3-10}  & & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** \\ \hline
1 & SRM + ReLU & 60.33 & 67.09 & 69.69 & 68.05 & 76.27 & 82.35 & 73.46 & 80.48 & 84.75 \\ \hline
2 & SRM + CReLU & 61.24 & 67.90 & 70.82 & 69.02 & 77.04 & 81.61 & 74.13 & 81.79 & 85.43 \\ \hline
3 & HB + SRM + ReLU & 61.93 & 69.14 & 71.68 & 69.51 & 77.97 & 82.85 & 75.03 & 82.45 & 86.38 \\ \hline
4 & HB + SRM + CReLU & 62.49 & 68.59 & 72.26 & 70.00 & 78.68 & 84.11 & 76.02 & 83.36 & 87.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance analysis of a high-boost filter and CReLU layer. classifiers. It was also found in the experimental analysis that the SVM classifier performed better than the softmax classifier in most of the scenarios, as can be seen in Table 3. The sequential minimal optimization was used for fast and optimized convergence. In Table 4, the results are displayed for Case I using the proposed technique. The results were also compared with other popular techniques such as SRNet [28], Ye-Net [27], Yedroudj-Net [30], and Zhu-Net [32]. The detection accuracy of the proposed technique and other techniques was the highest for WOW, followed by the S-UNIWARD and HILL steganography algorithms. The proposed technique results are more impressive than other techniques except for S-UNIWARD with 0.2 bpp and HILL with 0.4 bpp. In the two scenarios, the detection accuracy of Zhu-Net was better than the proposed method. The proposed method gave 77.02, 83.95, and 87.26% detection accuracies for WOW with payloads of 0.2 bpp, 03 bpp, and 0.4 bpp, respectively. For better results, additional numbers of images were considered for training the network. In Case II, fourteen thousand images were taken for CNN training. There was a substantial enhancement in detection accuracy, as can be seen in Table 5. The proposed technique gave 64.49%, 74.10%, and 77.13% detection accuracies for HILL with the 0.2, 0.3, and 0.4 bpp payloads, respectively. The proposed technique gives 75.06%, 83.64%, and 86.85% for S-UNIWARD with 0.2, 0.3, and 0.4 bpp payloads, respectively. The proposed technique gave 81.50%, 88.81%, and 91.78% for WOW with the 0.2, 0.3, and 0.4 bpp payloads, respectively. Zhu-Net provided better results by a slight margin for HILL \(\rho\) = 0.4 bpp and WOW \(\rho\) = 0.4 bpp. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Steganography Technique/**} & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \cline{2-10}  & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** \\ \hline SRNet & 55.51 & 63.64 & 67.62 & 64.97 & 73.88 & 79.34 & 72.52 & 79.41 & 83.59 \\ \hline Ye-Net & 54.31 & 59.40 & 63.85 & 60.20 & 68.01 & 74.83 & 69.39 & 72.97 & 78.65 \\ \hline Yedroudj-Net & 54.09 & 58.72 & 67.98 & 59.74 & 69.29 & 74.97 & 69.96 & 75.52 & 81.07 \\ \hline Zhu-Net & 61.74 & 66.61 & 74.56 & 70.23 & 78.12 & 82.81 & 74.37 & 78.41 & 86.23 \\ \hline Proposed Method & 63.25 & 69.91 & 73.07 & 69.54 & 79.88 & 84.79 & 77.02 & 83.95 & 87.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance assessment while considering Case I.