# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 1 Introduction  
Image steganography can be defined as a non-uniform operation unlike other conventional operations such as high-pass filtering, contrast enhancement, etc. The secret contents are embedded by changing the pixel values in random order, mostly unnoticeable due to the context-aware approach. Statistical changes are unnoticeable because most of the steganography algorithms increase the existing pixel values by only +1 or decrease by \(-1\). Steganalysis discloses the minute changes of image steganography. In this paper, steganalysis was performed for three popular context-aware steganography algorithms-HILL [1], S-UNIWARD [2], and WOW [3]. In Figure 1a, the cover image is shown and difference array images (DI) of the cover image and the stego-image are shown in Figure 1b-d for HILL, S-UNIWARD, and WOW, respectively, with 0.4 bits per pixel (bpp) payload. Furthermore, an image was formed to see the nature of different steganography algorithms. In Figure 2a, the cover image has four triangles. There is a difference of one pixel intensity in each triangle. The difference array images (DI) of the cover and stego-images after applying the HILL, S-UNIWARD, and WOW steganography algorithms with payloads of 0.4 bpp are displayed in Figure 2. The artifacts of steganography can be seen in the DI of the cover and HILL image (Figure 2b). The changes were more visible in the minor and major diagonal edge areas than in other areas. However, the changes were more visible in the DI of the S-UNIWARD image (Figure 2c) in comparison to the DI of the HILL image (Figure 2b). The nature of WOW was different, as evident from Figure 2d, as only the diagonal area was used for data embedding. It can be seen from Figures 1 and 2 that HILL changed the image pixels in a more uniform manner than S-UNIWARD and WOW. However, changes by WOW were mostly only in dense regions. HILL changes were not in a particular region, which makes the detection of HILL more challenging than S-UNIWARD, followed by WOW. This claim has been verified in the previous literature and in Section 3 of the experimental results. Previously, most of the techniques relied upon the texture operator based features and Markov features. Difference arrays and residual arrays of the image are considered to extract higher dimension features. [PERSON] and [PERSON] [4] considered the magnitude and phase statistics in the frequency domain to detect Outguess [5] and F5 [6] steganography techniques. [PERSON] et al. [7] extracted the Markov and texture features to classify the cover and stego-images. The high dimensional feature vector of size 22,153 is used for HUGO [8] steganography detection. [PERSON] et al. [9] utilized the second-order Markov features from difference arrays in eight directions for the detection of the least significant bit (LSB) matching steganography. [PERSON] et al. [10] extracted the Markov features in the DFT and DCT domains to detect seven different steganography techniques. [PERSON] and [PERSON] [11] extracted the 34,671 features using the Markov model using thirty high-pass filtered images. These high-pass filters are commonly known as SRM filters. [PERSON] et al. [12] proposed the technique for WOW. Markov features can be extracted from the texture and edge area, especially for superior results. To reduce the size of the feature vector of the method [11], the maxSRM and maxSRMd2 techniques were proposed by [PERSON] et al. [13]. The technique was applied to S-UNIWARD, S-UNIGARD, and WOW. [PERSON] et al. [14] suggested the local correlation pattern to identify HUGO, LSB matching revisited LSBMR [15], and the S-UNIWARD techniques. [PERSON] et al. [16] first applied the high-pass filter and then extracted the texture features. PCA was applied twice to reduce the feature dimension and improve the accuracy. [PERSON]'s technique was verified on the WOW and HUGO steganography  
Figure 1: Natural cover image and difference arrays of the cover and stego-image. (**a**) Cover image. (**b**) DI of the cover and HILL stego-image.