Steganalysis for Secret Message Length Estimation using GBRAS Net Regressor

The main objective of steganalysis is to predict whether a suspect image is a cover image or a stego image. After predicting the presence of a secret message, further steganalysis research continues by estimating the length of the secret message. Research on estimating the length of secret messages aims to validate the existence of secret messages by providing measurable evidence that a digital medium, particularly an image, contains a secret message of a certain length. Estimation of the length of secret messages embedded using the S-Uniward adaptive steganography algorithm in previous works, which utilized a pretrained ResNet-50, shows high MAE values. This performance indicates the need for improvements in the deep learning regressor architecture. Therefore, this study proposes the development of GBRAS Net for estimating the length of secret messages by modifying the classification layer into a regression layer. The modification involves replacing the Softmax loss function with Mean Squared Error (MSE) and using continuous values as a substitute for payload class labels. This study aims to develop a predictive model to estimate the length of secret messages using the GBRAS Net regressor on the Bossbase 1.01 dataset. The proposed model shows the lowest MSE (0.0182), RMSE (0.1349), and MAE (0.1064) values among ResNet 50, VGG-16, and Ye Net regressor.