# An Ensemble Transfer Learning Model for Detecting Stego Images
## 2 Literature Review  
Article [3] discussed how to combine trained CDNs in a multimodal framework, and it examined their detection accuracy. The framework detects each classification modality independently and combines their estimations to create a universal image steganography detector. Six of the latest CDN-based image steganography detection techniques--GNCNN, IGNCNN, XuNet, YeNet, YedroudjNet, and the improved IGNCNN--were trained on stego images generated using WOW, SUNIWARED, and HILL steganography algorithms with payloads of 0.2, 0.3, and 0.4 bits per pixel. Due to the projected similarities between the image steganography systems, the detection accuracy decreased slightly. However, the multimodal image steganography detection based on the improved IGNCNN universal image steganography detection performed best compared to the other five examined detectors [3]. Article [9] discussed detecting steganography-modified JPEG images, and it analyzed image steganography detection using shallow and deep learning methods. Three common stenographic algorithms--JPEG universal wavelet relative distortion (J-Uniward), nsF5, and uniform embedding revisited distortion (UERD) at two density levels--hid information in BOSS database photos. DCTR and GFR were the best feature spaces validated. At 0.4, the nsF5 algorithm detected bpnzac density with 99.9% accuracy, but the J-Uniward algorithm was barely detectable at 0.1 (a maximum of 56.3%). The study concluded that ensemble classifiers were a promising alternative to deep-learning-based detection [9]. In [10], the authors presented a deep-learning-based approach for steganography detection in digital images. The authors began by describing the importance of steganography detection in the field of digital forensics and highlighted the challenges associated with it. They then proposed a CNN-based model for detecting the presence of hidden data in digital images. The proposed model took as input the pixel values of an image and learned to identify the presence of steganography through a series of convolutional, pooling, and fully connected layers. The authors evaluated the performance of the proposed model on a dataset of stego images and showed that it outperformed existing steganography detection techniques in terms of accuracy, precision, and recall. The results of the study suggested that deep-learning-based approaches can be effective for steganography detection in digital images and can help improve the accuracy and reliability of forensic investigations. In [11], the authors began by describing the importance of steganalysis in digital forensics and highlighted the limitations of traditional steganalysis techniques. They then proposed a deep-learning-based model for steganalysis that used a combination of convolutional and fully connected neural networks. 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 and sensitivity to different types of spatial image steganography. They also performed a sensitivity analysis of the proposed model to evaluate the impacts of different hyper parameters and architecture choices on the model's performance. 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 careful selection of hyper parameters and architecture choices can further improve the performance of a model [11]. The authors of [12] began by describing the importance of steganalysis in digital forensics and highlighted the limitations of traditional steganalysis techniques. They then proposed a deep-learning-based model for steganalysis that used a combination of non-local blocks and multi-channel convolutional networks to identify the presence of hidden information in an image.