# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 2 The Proposed Scheme  
Image steganalysis is required to restrict the misuse of steganography techniques. The detection of steganography is more challenging than other types of image manipulations such as image enhancement, median filtering, and so on, due to its non-uniform and minute modifications in pixel intensity. A new robust scheme was proposed for detecting the steganography in the image. Effective measures were applied to improve the existing steganalysis techniques. Unlike previous techniques, a high-boost filter was applied to reveal crucial statistical information. A high-boost filter [35; 36] highlights the high-frequency components without compromising low-frequency components. A high-boost filter \(H_{k}\) can be defined as follows. In here, \(C_{HP}\) is any high-pass filter and \(k\) is a constant. \[H_{k}=C_{0}+kC_{HP}\]  
\[H_{k}= \begin{bmatrix}0&0&0\\ 0&1&0\\ 0&0&0\end{bmatrix} +k \begin{bmatrix}0&-1&0\\ -1&4&-1\\ 0&-1&0\end{bmatrix} \]  
\[H_{k}= \begin{bmatrix}0&-k&0\\ -k&4k+1&-k\\ 0&-k&0\end{bmatrix} \]  
In Figure 2(a), a pristine cover image is shown. The SRM filtered image after applying the \(S\) SRM filter on a pristine image (Figure 2(a)) is displayed in Figure 2(b). In Figure 2(c), a high-boost filtered image is displayed using a high boost filter \(H_{1}\) on a pristine image (Figure 2(a)). Furthermore, the \(S\) SRM filter was applied to the high-boost filtered image (Figure 2(c)) and the resultant image is displayed in Figure 2(d). The coarseness of Figure 2(d) helped in increasing the detection accuracy of the proposed technique. A total of thirty SRM filters was applied to enhance the statistical information. The SRM filters have several kernel weights that provide additional statistical information in multiple directions. \[S= \begin{bmatrix}-1&2&-1\\ 2&-4&2\\ -1&2&-1\end{bmatrix} H_{1}= \begin{bmatrix}0&-4&0\\ -4&17&-4\\ 0&-4&0\end{bmatrix} \]  
Figure 3: _Cont._To show the effectiveness of the high-boost filter, the probability plots for normal distribution are displayed for the cumulative variance of the cover and stego-images in Figure 4. Ten thousand images of BOWS2 [37] were considered after resizing to the dimensions of 256 \(\times\) 256 pixels. In Figure 4a-c, the plots are shown for the S-UNIWARD steganography technique used with payloads of 0.2 bpp, 0.3 bpp and 0.4 bpp, respectively, and in Figure 4d-f, the WOW steganography technique is considered. A similar behavior was also followed by HILL. DNF is the variance of the non-filtered cover image and stego-image. DHB is the variance of the high-boost filtered cover image and high-boost filtered stego-image. The normal distribution data of DHB had much greater disparity than DNF. This disparity helps in improving the detection of the cover and stego-images. Figure 4: Probability plots for the normal distribution of non-filtered and high-boost filtered images. (**a**) S-UNIWARD \(\rho\) = 0.2. (**b**) S-UNIWARD \(\rho\) = 0.3. (**c**) S-UNIWARD \(\rho\) = 0.4. (**d**) WOW \(\rho\) = 0.2. (**e**) WOW \(\rho\) = 0.3. (**f**) WOW \(\rho\) = 0.4. Figure 3: Cover image, SRM, high-boost, high-boost with SRM filtered image. (**a**) Cover image. (**b**) SRM filtered image. (**c**) High-boost filtered image. (**d**) SRM filtered image of Figure 3c. Furthermore, in Table 1, the percentage increase in the modified pixels after applying high-boost filtering is displayed. First, the number of modified pixels was calculated using the cover and stego-images. Second, the number of modified pixels was calculated using a high-boost filtered cover and high-boost filtered stego-image. As can be seen, there was more than a 50% increase in modified pixels for WOW with \(\rho\) = {0.2, 0.3, 0.4}. There was an increase in the modified pixels of 40.58% for S-UNIWARD and 45.43% for HILL with \(\rho\) = 0.2 bpp. Table 1 also proves the effectiveness of the high-boost filter. The overall behavior of the HILL, S-UNIWARD, and WOW steganography techniques are displayed in Figure 5 according to their payloads.