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
   
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    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