Detecting Secret Messages in Images Using Neural Networks

Image-based applications are widely spread nowadays. Steganography is a type of data hiding methods that manage covering the presence of a confidential communication between two ends. That is obtained by concealing the secret medium into a cover medium to deliver a stego medium that ought to be unnoticeable to a third party. The contrary of steganography, image steganalysis, is about identifying the presence of stego images. Image steganography schemes are becoming more and more secure every day. Cyber criminals can utilize these schemes to conduct a secret and malicious communication. Therefore, image steganalysis is of great importance to interfere such communications. In this paper, a transform domain based steganalysis scheme is proposed that utilizes the architecture of AlexNet, which is an object classification DL scheme. Some modifications are performed on the existing AlexNet architecture to enhance the detection performance of the model. Experiments showed that FB-GAR steganography scheme was successfully detected with an accuracy of 74.72%. Also, the relationship between the capacity and the quality of stego images was studied in this paper for both FB-GAR and J-UNIWARD. Furthermore, the relationship between the correlation of the cover images and the capacity and quality of stego images was discussed.