# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 1 Introduction
(**c**) DI of the S-UNIWARD stego-image. (**d**) DI of the WOW stego-image. methods. [PERSON] et al. [17] proposed the variant of the local binary pattern (LBP) using adaptive thresholding. Second-order Markov and TLBP features were combined for better results. [PERSON]'s technique was applied to S-UNIWARD, HILL, CMD-HILL [18], and MiPOD [19]. [PERSON]'s work was extended by [PERSON] et al. [20] by modifying the rotation invariant uniform pattern (RIU) mapping using a feature separation study. The features were also aggregated with the SRM frequency domain features. This technique was applied to detect S-UNIWARD, HILL, and MiPOD stego-images. [PERSON] et al. [21] extracted the TLBP and Markov features from residual arrays of non-negative matrix factorization, high-pass filters, and derivative filters to detect HILL, CMD-HILL, and MiPOD stego-images. Markov features were found to be effective in most of the manual feature extraction schemes. Due to its effectiveness, most of the schemes utilized the Markov model and improved the results by using a different type of residual array, combined with the texture features as discussed above. In parallel, many techniques have evolved that are based on convolutional neural networks. Currently, most of the techniques utilize deep learning networks not only for steganalysis as well in many other applications. However, this paradigm shift requires efficient hardware that is easily available in the current era. [PERSON] et al. [22; 23], for the first time, used the CNN to identify WOW, HUGO, and S-UNIWARD steganography techniques. In CNN, various types of layers such as the convolutional layer, ReLU layer, batch normalization layer, and pooling layer are used. Images were operated with one \(5\times 5\) high-pass filter before processing to the CNN. However, the results are not superior in comparison to the manual feature extraction techniques. [PERSON] et al. [24] also used the single filter for preprocessing. [PERSON] et al. introduced the absolute layer and utilized the tanh activation function to detect the S-UNIWARD and HILL technique. [PERSON] et al. [25; 26] continued to utilize the same single high-pass filter [22] and apply the proposed CNN on multiple residual arrays to identify HILL, MiPOD, S-UNIWARD, and WOW stego-images. The SRM filters [11] based on the Markov model were also found to be helpful in the CNN network. [PERSON] et al. [27] considered the residual of thirty SRM filter [11] images in the CNN, a truncated linear unit (TLU) instead of ReLU and the selection channels. The results of [PERSON]'s technique were better than previous methods on HILL, S-UNIWARD, and WOW. [PERSON] et al. [28] proposed the popular steganalysis residual network (SRNet) to detect HILL, J-UNIWARD, S-UNIWARD, UED-JC [29], and WOW. Three types of block arrangements were used in the network using residual connections and a pooling layer. Statistical moments from the trained network were extracted to detect the cover and HILL, MiPOD, S-UNIWARD, and WOW stego-images. In [PERSON] et al. [30], a CNN with thirty SRM filters were used in the non-trainable layer. CNN is highly influenced by deep networks [24; 27]. The results are shown for S-UNIWARD and WOW. [PERSON] et al. [31] introduced the shared batch normalization layer and utilized twenty SRM filters. [PERSON] et al. [32] preprocessed the image using thirty SRM filters. In CNN, bottleneck and spatial pyramid pooling were introduced for the better detection of HILL, S-UNIWARD, and WOW stego-images. [PERSON] et al. [33] claimed better results on S-UNIWARD and WOW by changing the arrangements of the layers. The preprocessing was performed using thirty SRM filters. [PERSON] et al. [34] applied the transfer learning approach by using the weights from a low embedding stego-image trained network. [PERSON] et al. considered the spatial and frequency domain together to identify S-UNIWARD and WOW stego-images while the preprocessing was performed using thirty SRM filters.