<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="5.3.1" xmlns="http://www.crossref.org/schema/5.3.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schema/deposit/crossref5.3.1.xsd">
 <head>
  <doi_batch_id>aspg-2-3354-1791417220</doi_batch_id>
  <timestamp>20261007235340</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
  </depositor>
  <registrant>American Scientific Publishing Group</registrant>
 </head>
 <body>
  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Cybersecurity and Information Management</full_title>
    <abbrev_title>JCIM</abbrev_title>
    <issn media_type="print">2769-7851</issn>
    <issn media_type="electronic">2690-6775</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>15</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Coverless Image Steganography Based on Machine Learning Techniques</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Teba Hassan</given_name>
      <surname>AlHamdani</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Suhad A.</given_name>
      <surname>Ali</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Majid Jabbar</given_name>
      <surname>Jawad</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>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.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>177</first_page>
     <last_page>194</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3354</item_number>
    </publisher_item>
    <ai:program name="AccessIndicators">
     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/JCIM.150214</doi>
     <resource>https://www.americaspg.com/journal/2/article/3354</resource>
     <collection property="text-mining">
      <item>
       <resource mime_type="application/pdf">https://www.americaspg.com/storage/51733418842.pdf</resource>
      </item>
     </collection>
    </doi_data>
   </journal_article>
  </journal>
 </body>
</doi_batch>
