A Multilayer Secure Image Steganography Framework Using Edge-Adaptive Embedding and Pre-Encryption

High-capacity image steganography aims to conceal large volumes of data while preserving imperceptibility and resistance to statistical and visual detection. This study proposes a multilayer secure image steganography framework using edge-adaptive embedding and pre-encryption. The method utilizes multiple edge detectors, namely Canny, Laplacian of Gaussian (LoG), and Prewitt, to accurately classify edge and non-edge regions, enabling efficient use of high-tolerance embedding areas. A Bee Colony Footprint Edge Optimization (BCFEO) algorithm is employed to select optimal embedding locations through a distortion-aware adaptive process, improving payload distribution under varying capacity conditions. For enhanced security, the secret message is encrypted prior to embedding using AES in Counter (CTR) mode, ensuring confidentiality without altering payload size and allowing exact recovery. A 5-LSB filtering mechanism is applied during preprocessing to reduce redundancy and control embedding distortion. The proposed framework is evaluated on a set of several 256×256 resized RGB images, including benchmark images from the USC-SIPI database and independently captured natural images, using standard performance metrics such as PSNR, SSIM, NCC, UIQI, and statistical steganalysis techniques. Experimental results demonstrate that the method achieves high embedding capacity with minimal visual degradation and improved performance compared to conventional edge-based approaches. The integration of adaptive optimized embedding and pre-encryption provides an efficient and reliable solution for secure image-based communication systems. The broader validation using larger datasets, different image resolutions, and more diverse image categories remains a future research direction.