## 1 Introduction  
Image steganography schemes are applied to hide secret data/information that cannot be noticed by the naked eye. In most content-adaptive steganography schemes, syndrome-trellis code (STC) is considered to make the schemes less vulnerable. In this paper, four notable content-adaptive steganography schemes, such as HILL [1], Mi-POD [2], S-UNIWARD [3], and WOW [4], are analyzed. In steganography, there is only one unit value (addition or subtraction) in some image pixels, which is in accordance with the steganography scheme and message. STC ensures that there is no visible change in the image. In comparison to other classification problems, such as object recognition and texture classification, steganalysis is more challenging due to the low value of stego noise. Difference arrays of cover and stego images in the different steganography schemes are displayed in Figure 1, where the cover image [5] is shown in the first row. Difference arrays of cover and HILL, Mi-POD, S-UNIWARD, and WOW stego images are shown in the second and third rows for 0.2 bit per pixel (ppp) and 0.4 bpp payloads, respectively. Although it is difficult to interpret the difference between different steganography schemes, the changes are more evident with a higher value of the payload. The effect of steganography with different schemes cannot be understood effectively using natural images, as displayed in Figure 1. A computer-generated (CG) image with (0-255) is displayed in the first row of Figure 2 to understand a better effect of different steganography schemes. There is a difference of one unit value in adjacent pixels, with the exception of major diagonal pixel values, as well as a difference of 255 unit values in major diagonal pixel values. In different images of cover and stego images, it can be observed that each scheme has different behavior. In the HILL scheme, the changes in pixel values are more diversified than Mi-POD, followed by S-UNIWARD. However, WOW behavior is entirely different. The variations in the behavior of schemes are also reflected in experimental analysis. Figure 1: Cover image with residual arrays of cover and stego images. Several attempts have been made for the detection of image steganography. Initially, the thriving steganalysis is performed using Markov model-based features. Moreover, some researchers proposed steganalysis schemes based on texture descriptors. The first order and higher order difference arrays and residual 2D arrays are exploited for large-size feature vectors. As the large-size feature vector is more effective in steganalysis due to low stego noise, a feature vector is extracted from the spatial and/or frequency domain. [PERSON] and [PERSON] [6] exploited the statistics of magnitude and phase information in the frequency domain for the detection of F5 [7] and Outguess [8] steganography schemes. [PERSON] et al. [9] used the texture and Markov features, with a dimension of 22,153, for the detection of HUGO [10] steganography scheme. [PERSON] and [PERSON] [11] introduced thirty high-pass kernels that are frequently recognized as SRM kernels. These kernels are based on the first and higher order difference arrays and multiple quantization factors. Additionally, SRM kernels are applicable in most deep networks. The feature vector with a dimension of 34,671 is extracted using a Markov model, where the scheme is verified on HUGO and \(\pm\)1 variants  
Figure 2: CG cover image and residual arrays of cover-stego images. steganography schemes. [PERSON] et al. [12] extracted Markov features of the second order from the first order difference arrays for the detection of the \(\pm 1\) steganography scheme. The variants of the scheme [11], abbreviated as maxSRM and maxSRMd2, are proposed by [PERSON] et al. [13]. The formation process of the co-occurrence matrix is modified to decrease the feature vector size in the maxSRM and maxSRMd2 schemes. The Denemark scheme is verified on WOW, S-UNIWARD, and S-UNIGARD steganography schemes. Furthermore, [PERSON] et al.