Steganalysis Method

Steganalysis aims to detect hidden messages embedded within digital media, a crucial task given the increasing use of steganography for malicious purposes. Current research focuses on improving the robustness and efficiency of steganalysis methods, particularly using deep learning architectures like convolutional neural networks (CNNs) tailored to various data types (images, video, audio) and embedding techniques. Significant efforts address challenges like covariate shift between training and real-world data, and developing more efficient models with reduced computational demands. These advancements have important implications for digital forensics and cybersecurity, enhancing the ability to detect and counter covert communication.