Steganalysis Model

Steganalysis models aim to detect hidden messages embedded within various media types (images, videos, text) using advanced signal processing and machine learning techniques. Current research emphasizes improving detection accuracy against increasingly sophisticated steganographic methods, focusing on convolutional neural networks (CNNs), diffusion models, and algorithms that address issues like cover source mismatch and dataset bias through techniques such as domain adaptation and continual learning. These advancements are crucial for enhancing cybersecurity, protecting intellectual property, and assisting in forensic investigations by improving the detection of covert communication channels.