Show simple item record

AuthorSubramanian N.
AuthorElharrouss O.
AuthorAl-Maadeed, Somaya
AuthorBouridane A.
Available date2022-05-19T10:23:09Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3053998
URIhttp://hdl.handle.net/10576/31108
AbstractImage Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.
SponsorThis work was supported by the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP11S-0113-180276. Open Access funding provided by the Qatar National Library.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep learning
Learning systems
Cover-image
Evaluation metrics
Hiding informations
Image steganography
Learning methods
Learning techniques
Learning technology
Three categories
Steganography
TitleImage Steganography: A Review of the Recent Advances
TypeArticle
Pagination23409-23423
Volume Number9


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record