# An Ensemble Transfer Learning Model for Detecting Stego Images
## 3 Methodology
This is the process of disassembling an image. An image is either a collection of segments that cover the entire image or a group of contours drawn from the image. A region's pixels represent a computed property such as color, intensity, or texture. The same traits differ greatly between neighboring places. Segmentation is used to identify objects and boundaries in pictures (such as lines, curves, etc.). Image segmentation involves labeling each pixel in a picture to share visual features. Morphology, a prominent image processing method, changes shapes. By adding a structuring characteristic, morphological procedures create a similar-sized output image. Morphological operations compare each output pixel's value to its input image neighbors. The image structuring element's size and form determine how many pixels are added or subtracted from the image's objects. Before applying the suggested approach to an image, scanning algorithms usually scan the entire image. After pre-processing, the image is separated into binaries by scanning it vertically and horizontally. The sum of the rows must be larger than one to detect the topmost cell region pixel and to record an index when scanning an image vertically from top to bottom. After finding the index, scanning continues until the total of all rows is not zero, indicating the object's end. Algorithms repeat horizontal scanning to close structures. This improves the algorithm by speeding up processing. The classifier's performance is greatly affected by the quality of feature selection. Features must characterize each image subtype and be distinct from one another for a valid classification. All computed features can be partitioned into form features, intensity features, and texture features for easier comparison and assessment. To classify an item, we use its \"feature vector\", an n-dimensional vector containing a set of values reflecting various attributes, to feed to a classification algorithm, and data are extracted from images using feature extraction to produce a dataset of 22 features. For this study, 22 features were employed, including 5 co-occurrence matrices, the mean, the STD, the skewness, and the kurtosis, as well as 6 color moments and 7 moment-invariant properties. After collecting the data and storing them in a CSV file, the following phase was to employ a machine learning or deep learning technique to obtain the highest possible precision. Figure 3: Covert communication based on HUGO steganography [23]. After collecting the data from the feature extraction, the authors selected 22 features that were highly correlated. Four models were applied as a classification method in the next step. In this study, we used the following techniques: CNN, AlexNet, Resnet50, and Inception; the figures below show the specifications used for each network (Figures 4-7). After collecting the evaluation for each model, the system voted for the best model. Deep learning ensemble models are used to increase a system's performance. Ensembles reduce prediction variance by integrating numerous model outputs, improving accuracy, reducing overfitting, and improving model generalization. Several models trained on distinct data subsets can capture diverse data features and increase system generalization, and ensemble makes a model more resilient to noise and outliers. Errors and outliers can be decreased by merging model outputs. The ensemble optimizes computing resources and enables the efficient and concurrent training of numerous smaller models instead of that of a single large model. It is a strong deep-learning technique that improves model performance, generalization, robustness, and computing efficiency. Figure 4: Diagram of a 1D CNN model. Figure 5: Diagram of an AlexNet model. Figure 6: Diagram of a ResNet-50 model.