ABSTRACT
Steganography Detection in Multimedia Data addresses the increasing threat of covert communication through steganography in multimedia data by proposing a novel detection method leveraging the power of ResNet. Steganography, the art of covering information within inoffensive files, poses a significant challenge for conventional detection techniques. Steganography Detection in Multimedia Data harness the capabilities of ResNet, to enhance the accuracy and efficiency of steganography detection. The approach involves training a ResNet model on a diverse dataset of multimedia files, both pristine and steganographically manipulated. Key contributions include the development of a resilient and high-performing steganography detection model, capable of discerning even the most subtle alterations in multimedia content. The integration of ResNet develops the ability of the model to capture complicated relationships within the data, leading to improved accuracy and reduced false positives. The findings underscore the significance of leveraging deep learning techniques, specifically ResNet, in the ongoing battle against covert communication channels.
