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A multi-task learning CNN for image steganalysis
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Yu, Xiangyu, Tan, Huabin, Liang, Hui, Li, Chang-Tsun and Liao, Guangjun (2019) A multi-task learning CNN for image steganalysis. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, 11-13 Dec 2018 ISBN 9781538665367. doi:10.1109/WIFS.2018.8630766 ISSN 2157-4774.
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WRAP-multi-task-learning-CNN-image-steganalysis-Li-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1211Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/WIFS.2018.8630766
Abstract
Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN.
Item Type: | Conference Item (Paper) | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Image steganography , Data encryption (Computer science), Image processing -- Digital techniques | |||||||||||||||
Publisher: | IEEE | |||||||||||||||
ISBN: | 9781538665367 | |||||||||||||||
ISSN: | 2157-4774 | |||||||||||||||
Official Date: | 31 January 2019 | |||||||||||||||
Dates: |
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DOI: | 10.1109/WIFS.2018.8630766 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 18 May 2020 | |||||||||||||||
Date of first compliant Open Access: | 18 May 2020 | |||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||
Title of Event: | 2018 IEEE International Workshop on Information Forensics and Security (WIFS) | |||||||||||||||
Type of Event: | Workshop | |||||||||||||||
Location of Event: | Hong Kong | |||||||||||||||
Date(s) of Event: | 11-13 Dec 2018 |
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