# An Ensemble Transfer Learning Model for Detecting Stego Images
## 3 Methodology  
The procedure for the proposed method is shown below in Figure 1. The first step involves collecting images and mixing them between a normal image and a stego image. The second step pre-processes images to clean the low-quality images that serve as the system's entry point. In the final stage of pre-processing, images are segmented and then exposed to a scanning algorithm that extracts the features to create a dataset. The resulting dataset is then considered an input to the proposed categorization scheme. The automatic classification of detecting stego images is displayed in the flowchart in Figure 1.  
Figure 1: Procedure of the proposed method. The procedure begins by applying two efficient steganography methods (edge-adaptive and HUGO). Figure 2 shows the steps of the edge-adaptive technique for hiding information in the images. Edge-adaptive steganography is a method that embeds secret information into digital images by exploiting the edges and textures in the image. The technique is based on the observation that the human visual system is less sensitive to changes in the high-frequency regions of an image, which typically correspond to edges and textures. The general flowchart for edge-adaptive steganography involves several steps, such as image pre-processing, feature extraction, the embedding of secret information, and post-processing. The exact details of each step may vary depending on the specific implementation of the technique. The edge-adaptive method uses paired pixels to conceal information. If there is strong evidence of steganography, as indicated by large absolute differences between these pairings, [21] then it can be concluded that steganography was used. The method begins by dividing the cover image into blocks and computing the edge information of each block using the Sobel operator. The authors proposed the use of edge information to improve the embedding process and make it less detectable via steganalysis methods. Specifically, we proposed selecting a block with edge information similar to that of the secret information block in which the secret information would be embedded. The embedding process itself was based on LSB (least significant bit) matching, where the LSBs of a selected block are modified to hide secret information. We also proposed the use of a dynamic quantization scheme to ensure that the embedding process did not degrade the perceptual quality of the cover image. We evaluated the proposed method using various metrics, including embedding capacity, perceptual quality, and security. The experimental results showed that the proposed method outperformed existing LSB-based steganography methods in terms of both embedding capacity and security while maintaining good perceptual quality. Overall, the proposed method improved an existing LSB-based steganography technique by incorporating the edge information of the cover image and using dynamic quantization to improve the embedding quality. The method was shown to have better performance than existing methods had in terms of embedding capacity and security while maintaining good perceptual quality. Figure 2: Edge adaptive image steganography (**a**) data embedding and (**b**) data extraction [21]. The HUGO method was the first known steganography method to employ syndrome trellis codes [22]. This technique employs the difference between four neighbors (pixels) as a feature set to execute secret embedding with minimal distortion as shown in Figure 3. The obtained image enhancement step is often performed as part of the pre-processing stage and serves to make the initial image more suitable for further calculation. The next step is to convert the image into grayscale or to have only black and white tones because black includes information with an intensity value of \"0\", white contains information with an intensity value of \"255\", and grayscale images only carry intensity levels of between 0 and 255. By separating an image into its parts, lines, circles, and forms can be isolated.