Robust Steganalysis of LSB-Embedded Malicious Content Using Deep CNN

In the digital age, information transmission through hidden channels, such as steganography, has become more sophisticated with the advent of advanced embedding algorithms. This growing complexity underscores the critical need for robust techniques capable of detecting hidden information in images, regardless of the specific steganographic method employed.This project introduces a new approach to blind steganalysis using neural networks, with a focus on deep convolutional neural networks (CNNs). The proposed method is designed to detect steganographically embedded malicious payloads, including JavaScript, HTML, PowerShell scripts, URLs, and Ethereum addresses, without prior knowledge of the embedding algorithm.Experimental evaluations validate the effectiveness of this method in terms of the superior performance of the proposed design compared to the existing approaches. The result obtained shows a potential of using deep learning approaches in cybersecurity domains as a useful tool for unveiling hidden threats concealed within steganographic images. This work thus represents a substantial advancement in neural network applications, particularly in the detection of malicious payloads, thereby protecting digital communication systems against covert attacks.