Enhanced Data Embedding Using Adaptive Neural Networks with Modified Whale Optimization Algorithm

Abstract

This research paper introduces a new steganography algorithm for embedding large amounts of secret messages within a color image. Five security levels are incorporated to ensure reliable protection. To embed data in a randomized way, the algorithm uses a segmentation technique called New Adaptive Image Segmentation (NAIS). By analyzing the properties of each byte, this method determines the appropriate size of secret data to replace each byte and color in the image. Additionally, the algorithm features a machine-learning component inspired by an Adaptive Neural Network (ANN) combined with a modified version of the Whale Optimization Algorithm (MWOA). The findings show that strong imperceptibility is achieved with the stego-image, even with a large payload, reaching four bits per byte (4-bpb) at specific bytes. When the machine learning model ANN_MWOA is used, the highest PSNR reaches 79.58dB for the Baboon color image with a payload of (16384) bits, while PSNR decreases by 1% when applying ANN_WOA. Moreover, the proposed method outperforms previous approaches by an average of 2%. Additional metrics (MSE, SNR, Euclidean Norm, and others) are used to confirm that the proposed algorithm efficiently embeds hidden data.