Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3354 2019 2019 Coverless Image Steganography Based on Machine Learning Techniques Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq admin admin Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq Suhad A. Ali Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq Majid Jabbar Jawad Image steganography is a technique used to conceal secret information within digital images in such a way that the existence of the hidden data is not perceptible to the human eye. This method leverages the vast amount of data contained in image files, embedding the secret message by altering certain pixel values in a manner that is undetectable. The primary goal of image steganography is to ensure that the embedded information is secure and invisible, maintaining the original image's appearance and quality. Applications of image steganography include secure communication, digital watermarking, and copyright protection. Advanced methods often employ complex algorithms and machine learning models to enhance the robustness and imperceptibility of the hidden data, making it resistant to detection and manipulation.. The main idea of the proposed work is to utilize features extracted from images to construct a Hash Table, which will be employed for concealing and revealing a secret message. Since the same CNN model and input image (i.e., cover image) produce identical features, even if the cover image is slightly affected by noise, the same features (and consequently the same Hash Table) will be generated. The work demonstrated promising results in regenerating images when the cover image is slightly affected. However, as the noise level increases on the cover image, the regenerated images begin to lose more details. 2025 2025 177 194 10.54216/JCIM.150214 https://www.americaspg.com/articleinfo/2/show/3354