# An Ensemble Transfer Learning Model for Detecting Stego Images
## 2 Literature Review
The proposed model was trained and evaluated on a dataset of stego images that contained spatially embedded hidden information. The authors showed that the proposed model outperformed existing steganalysis techniques in terms of accuracy, precision, and recall. They also showed that the proposed model could be used to localize the regions of an image that contained hidden information. The results of the study suggested that deep-learning-based approaches can be highly effective for the steganalysis of spatially embedded hidden information in digital images and that the proposed model can help improve the accuracy and efficiency of forensic investigations [12]. Article [13] presented a deep-learning-based approach for detecting steganography in color images. The authors began by describing the importance of steganalysis in digital forensics and highlighted the challenges associated with detecting hidden information in color images. They then proposed a multi-frequency residual convolutional neural network (MRF-CNN) for steganalysis that extracted features from different frequency components of an image and learned to identify the presence of hidden information. The proposed model was trained and evaluated on a dataset of stego color images and compared with existing steganalysis techniques. The authors showed that the proposed MRF-CNN model outperformed existing steganalysis techniques in terms of accuracy, precision, and recall. They also showed that the proposed model could be used to localize the regions of an image that contained hidden information. The results of the study suggested that deep-learning-based approaches, such as the proposed MRF-CNN model, can be highly effective for the steg analysis of images and can help improve the accuracy and efficiency of forensic investigations. The authors of [14] began by describing the importance of hand movement identification in various applications, such as prosthetics and rehabilitation. They then proposed a machine-learning-based approach for identifying hand movements that involved the use of electromyography (EMG) signals recorded from muscles in the arm. The proposed approach used a combination of feature extraction techniques and classification algorithms to identify hand movements. The authors evaluated the proposed approach on a dataset of EMG signals recorded from multiple subjects and showed that the proposed approach achieved high accuracy in identifying hand movements. They also compared the performance of the proposed approach with that of existing approaches and showed that the proposed approach outperformed them. The results of the study suggested that machine-learning-based approaches can be highly effective for identifying hand movements and can have important applications in prosthetics and rehabilitation [14]. In [15], the authors presented a novel deep neural network for identifying contextual steganography methods. The suggested method employed a high-boost filter to reduce high-frequency noise while keeping low-frequency information intact. Thirty high-pass SRM filters were applied to the high-boost image, resulting in thirty high-boost SRM-filtered photos. The suggested CNN used two skip connections to simultaneously gather data from a large number of connections. Despite the standard ReLU layer, a cropped version was investigated. The convolutional neural network (CNN) was built using a bottleneck strategy for maximum efficiency. For comprehensive data persistence, only one layer of global average pooling was used. To further enhance the detection accuracy, SVM was used in place of the softmax classifier. Compared to state-of-the-art methods, the proposed method performed better in terms of detection accuracy and computational cost in the experiments. The HILL, S-UNIWARD, and WOW context-aware steganography algorithms were tested on the BOWS2 and BOSS base datasets, validating the suggested scheme [15]. An ensemble classifier was trained using rich features that detected hidden messages in images for 10 years.