INTELLIGENT STEGANOGRAPHY: CNN-BASED DATA HIDING IN EDGE REGIONS AND NON-OVERLAPPING BLOCKS

Deep learning methods added to steganography have greatly simplified the hiding of data in digital media without anyone noticing. This article discusses a clever steganography technique hiding information in edge areas using Convolutional Neural Networks (CNNs), hence covering picture blocks not overlapping. Carefully selected edge sections feature several varied textures, which helps to conceal injected data and reduce visual distortion. By means of local pixel variation, the proposed CNN-based approach automatically locates the optimal embedding sites. This guarantees great payload capacity without compromising undetectability. Unlike other techniques that depend on manual feature engineering, ours employs deep learning to modify the embedding strength depending on the image material, hence enhancing safety and resilience. Blocks that don't overlap save even more computing power. They let processing occur in real time and lower mistakes that can lead steganography to be found, thus We evaluated the proposed approach on several standard datasets and discovered that, in terms of embedding capacity, visual quality (as assessed by PSNR and SSIM), and resistance to statistical attacks, it performs better than conventional LSB and DCT-based approaches. The technology also performs effectively with various kinds of photos, which implies it can be utilized for digital watermarking and secure interaction.By means of a secure, flexible, high-capacity approach to conceal data, this work bridges the gap between machine learning and steganography. Current CNN-based steganography methods often suffer from low payload capacity, especially when data hiding is limited to edge regions that constitute a very small percentage of the image. Moreover, overlapping block methods compromise robustness and invisibility as they do not adapt to local image complexity and incur redundancy and computational overhead. Furthermore, many existing methods are vulnerable to modern steganalysis tools and are not transferable to different types of images due to limited use of deep features and static embedding strategies. Researchers in the future could investigate how antagonistic training could reduce system susceptibility to sophisticated steganography systems.