Architectural Modifications to Enhance Steganalysis with Convolutional Neural Networks

This paper investigates the impact of various modifications introduced to current state-of-the-art Convolutional Neural Network (CNN) architectures specifically designed for the steganalysis of digital images. Usage of deep learning methods has consistently demonstrated improved results in this field over the past few years, primarily due to the development of newer architectures with higher classification accuracy compared to their predecessors. Despite the advances made, further improvements are desired to achieve even better performance in this field. The conducted experiments provide insights into how each modification affects the classification accuracy of the architectures, which is a measure of their ability to distinguish between stego and cover images. Based on the obtained results, potential enhancements are identified that future CNN designs could adopt to achieve higher accuracy while minimizing their complexity compared to current architectures. The impact of modifications on each model's performance has been found to vary depending on the tested architecture and the steganography embedding method used.