# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 3 Experimental Analysis  
Image steganography is performed to hide some secret information. In some cases, steganography is misused. In this paper, the image steganalysis technique was proposed to restrict the misuse of image steganography. The proposed technique was verified for multiple cases. Two popular datasets BOSSBase [42] and BOWS2 [37] were used for detailed experimental study. Both datasets had ten thousand images with the dimensions of \(512\times 512\) pixels. The dimension of the images was changed to dimensions of \(256\times 256\) pixels using interpolation. Experimental analysis was performed on the dimensions of \(256\times 256\) pixels. The proposed technique was compared with the popular state-of-the-art techniques SRNet [28], Ye-Net [27], Yedroudj-Net [30], and Zhu-Net [32]. The detailed experimental analysis was performed by considering three cases of image dataset combination. Case I: The BOSSBase dataset was considered in Case I. Stego-images were created using three steganography algorithms--HILL, S-UNIWARD, and WOW. Four thousand images were considered for training, one thousand for validation, and five thousand for testing from both the classes, cover, and stego. Multiple pairs were formed according to the different payloads and steganography algorithms. Case II: The BOWS2 and BOSSBase image datasets were considered in Case II. In comparison to Case I, a higher number of images were considered in Case II for better results. Fourteen thousand images were considered for training the network, one thousand images for validation, and five thousand images for testing the trained network. An equal number of images were taken from the BOWS2 and BOSSBase datasets. The same number of stego-images was also created according to the steganography algorithm and payload. Case III: Like Case II, both datasets were considered. One hundred and twenty-five thousand cover images were created after applying data augmentation on both datasets. A hundred thousand images were used for training, five thousand for validation, and fifteen thousand for testing. Corresponding stego-images were also created according to the steganography algorithm and payload. The HILL, S-UNIWARD, and WOW stego-images with embedding payloads of 0.2, 0.3, and 0.4 bpp were considered in the experimental results. The results are displayed in terms of the classification accuracy in percentage. In Table 2, the effect of the high-boost filter and clipped ReLU (CReLU) is displayed while considering Case I and the softmax classifier. In most of the previous literature, SRM filters were applied as pre-processing or in non-trainable layers to improve the detection of stego-noise. Therefore, SRM filters were considered in each result. In the first row, the results are displayed using SRM filters and ReLU layers on the proposed network while not including a high-boost non-trainable layer. In the second row, the results are displayed using SRM filters and CReLU layers while not including the high-boost non-trainable layer. In the third row, the results are displayed using ReLU layers in the proposed network. In the fourth row, the results are displayed using the proposed network. The softmax classifier was used for classification in the four scenarios. Usually, a softmax classifier is utilized in CNN. In experimental analysis, some other classifiers have also been tried. The SVM classifier has been proven to be better than many