TRAPPING OF STEGO IMAGES ON THE BASIS OF STATISTICAL EVIDENCES
Main Article Content
Abstract
Image-based steganography is an information hiding technique for improving information security. Its purpose is to cover the original data in image to secure the data. Recently, a stego-image detection scheme was proposed, which uses the so-called extreme learning machine and features of digital images for analysis, hiding data through steganography and analysing it through steganalysis which is used to find whether an image contains secrete data. Steganography is developed to hide the data using some digital media and steganalysis is explained as the technique to find the hidden data in the digital media; can also be explained as analysing the steganography presence. The information can be hidden in images in different domains like discrete cosine transform and spatial. The algorithm changes the properties of the image due to embedded artefacts. In this project the main goal is to develop a steganalysis system to identify the presence of hidden information in images, based on Image Quality Measures as well as identify the steganography embedding domain using Support Vector Machine.
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.