## 1 Introduction
[14] utilized the Markov model, although the dense and edge region is exploited for better results. The local correlation pattern [15] is proposed for identifying HUGO, S-UNIWARD, and the LSB matching revisited LSBMR [16] steganography schemes. [PERSON] et al. [17] proposed the steganalysis scheme based on texture operator features and verified it on HUGO and WOW steganography schemes. In this scheme, images are preprocessed with high-pass kernels. Moreover, PCA is used to decrease the size of feature vector and increase the performance. In [18; 19; 20], popular texture operators, such as the variant of the local binary pattern (LBP) with some improvements, are proposed for steganalysis. Moreover, Markov features are combined for better performance. In [18; 19], schemes are verified on HILL, Mi-POD, and S-UNIWARD steganography schemes. A steganalysis scheme [20] is applied for the detection of HILL, CMD-HILL [21], and Mi-POD steganography schemes. At present, many recent steganalysis schemes depend on deep networks, as the high computing power hardware is not an obstacle. [PERSON] et al. [22; 23] introduced the deep network for the detection of HUGO, S-UNIWARD, and WOW steganography schemes. Images are preprocessed by one \(5\times 5\) high-pass kernel. However, the results are inferior to some manual schemes. In the [PERSON] et al. method [24], the tanh is applied rather than ReLU for the identification of HILL and S-UNIWARD steganography schemes. In [25; 26], numerous residual 2D arrays and one \(5\times 5\) high-pass kernel are used to enhance the performance on many types of stego images. [PERSON] et al. [27] used the residual array after processing with thirty high-pass kernels [11]. Truncated linear unit (TLU) is considered rather than ReLU to outperform previous schemes on WOW, HILL, and S-UNIWARD. A deep network known as steganalysis residual network (SRNet) [28] is introduced, where skip connections and pooling layers with other types of layers are used to create three categories of blocks. Moments are extracted from a trained deep network to identify HILL, S-UNIWARD, and WOW for uncompressed images and J-UNIWARD and UED-JC [29] for compressed images. [PERSON] et al. [30] utilized thirty high-pass kernels [11] in a non-trainable convolutional layer. In [31], a new concept of shared batch normalization is proposed to improve the performance. The images are preprocessed with twenty high-pass kernels [11], which is verified on HILL, HUGO, S-UNIWARD, and WOW. In [PERSON] et al. [32], thirty high-pass kernels [11] are utilized in a non-trainable layer. In particular, two concepts are incorporated with the suggested CNN, bottleneck approach and spatial pyramid pooling, for the detection of three popular types of stego images. Furthermore, [PERSON] et al. [33] utilized thirty high-pass kernels in a non-trainable convolutional layer with some improvements in the order of layers. [PERSON] et al. [34] preprocessed the image with thirty high-pass kernels and included the transfer learning approach. The authors exploited both spatial and frequency domains. The network was initialized with trained CNN from low-capacity stego images and verified on S-UNIWARD and WOW stego images. As can be seen from previous research on image steganalysis, the majority of the previous articles can improve the performance by exploiting the high-pass kernels in preprocessing to increase the stego noise for better classification. In this paper, two measures are considered to reduce the detection error without increasing the computational cost. The key measurements of the proposed scheme are outlined as follows:  
* In the proposed scheme, thirty-one kernels are used; thirty are high-pass kernels and one is the neutral kernel. * Two non-trainable convolutional layers are considered using thirty-one kernels; one layer is used at the beginning of the network and the second before the middle of the network. * To retain a complete statistical information, down-sampling is not performed.