A Comprehensive Exploration of Data Security Techniques Using Image Steganography

Steganography, the practice of concealing information within digital media, has seen significant advancements through various tools like Steghide, OpenPuff, and OpenStego. However, the effectiveness and robustness of these tools in resisting steganalysis remain critical challenges. This work investigates the application of advanced steganalysis algorithms, including Shannon Entropy, Gaussian Mixture Model, Scale-Invariant Feature Transform, and Spatial Rich Model, using the Python ecosystem. A comparison of these algorithms with pre-existing tools is conducted to highlight the gaps in current methodologies. The proposed approach integrates deep learning techniques such as Simple CNN, Batch Normalization, and ResNetlOl to enhance accuracy and reduce loss in steganography.