A deep learning-driven multi-layered steganographic approach for enhanced data security

In the digital era, ensuring data integrity, authenticity, and confidentiality is critical amid growing interconnectivity and evolving security threats. This paper addresses key limitations of traditional steganographic methods, such as limited payload capacity, susceptibility to detection, and lack of robustness against attacks. A novel multi-layered steganographic framework is proposed, integrating Huffman coding, Least Significant Bit (LSB) embedding, and a deep learning-based encoder-decoder to enhance imperceptibility, robustness, and security. Huffman coding compresses data and obfuscates statistical patterns, enabling efficient embedding within cover images. At the same time, the deep learning encoder…