# An Ensemble Transfer Learning Model for Detecting Stego Images
## 1 Introduction  
As the quantity of traffic being moved and communicated over the internet in various formats, such as movies and photographs, increases daily, there is a rising concern about the security of the massive amount of data transferred over the internet, including passwords and personal, professional, and financial information, as well as social security numbers and other sensitive data. The need to keep this information safe is rising as well. Network security has become an integral part of today's sophisticated communication infrastructure to keep information private and prevent tampering [1, 2]. Using machine learning to analyze and improve digital images, the practice of steganalysis is gaining in popularity. Steganography methods implemented inside such a framework are more secure than those employing manually constructed pricing. Steganalysis refers to the process of identifying hidden messages using steganography. In the world of cryptography, this is analogous to the practice of cryptanalysis. As a result, learning stenography is essential. A communication that has been interecepted and decrypted is present whereas cryptanalysis begins with a collection of suspicious data files without knowing which files, if any, hold a payload. The first step in steganalysis, which effectivelyrequires a forensic statistician, is to identify the data files that are most likely to have been tampered with in a massive, often exhaustive, collection [3]. There is a constant, growing requirement to safeguard the escalating volume of sensitive information exchanged and communicated online daily in various formats, including movies and photographs. The need for secure networks to protect sensitive data from prying eyes and malicious actors has made such security an integral aspect of today's communication infrastructure [4; 5; 6; 7; 8]. Cryptography, which is the practice of transforming plain text into encrypted text via an algorithm, has been widely used for decades to safeguard sensitive information. To read an original message, a recipient must first convert encrypted text into plain text ([PERSON] and [PERSON], 1988) [4]. To keep sensitive data safe, encryption techniques such as the advanced encryption standard (AES) (NIST-FIPS, 2001) and the data encryption standard (DES) (NIST-FIPS, 1977) are commonly utilized ([PERSON] and [PERSON], 2016) [5]. The fact that encrypted communication can be read by anyone is seen as a weakness in cryptography, which translates secret messages into human-readable forms. Therefore, hackers on the internet may use the heat, tries, and attempts strategy to decipher code. Because of this shortcoming in cryptography, steganography was brought into the field of data protection to circumvent this problem by disguising the fact that a communication was taking place. The motivation for detecting image steganography using deep learning arises from the increasing use of steganography in various applications, including data hiding and digital watermarking. Steganography involves hiding data within an image, and it can be used for both legitimate and malicious purposes, such as covert communications or concealing sensitive information. With the rise of deep learning techniques, there is an opportunity to develop more accurate and efficient steganalysis methods that can automatically detect hidden data within images. Deep learning models can learn complex features and patterns from large datasets, which can be used to identify steganography content in images. The motivation for developing such methods is to improve the security and privacy of digital communication and prevent the malicious use of steganography. By detecting steganographic content in images, it is possible to identify potential threats and take appropriate measures to protect sensitive information. Additionally, deep-learning-based steganalysis can offer a better performance than can traditional steganalysis methods, which may require manual feature extraction and may not be as accurate. The paper's organization is as follows.