File(s) under permanent embargo
A multi-task learning CNN for image steganalysis
conference contribution
posted on 2018-01-01, 00:00 authored by X Yu, H Tan, H Liang, Chang-Tsun LiChang-Tsun Li, G LiaoConvolutional 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.
History
Event
IEEE Signal Processing Society. Conference (10th : 2018 : Hong Kong, China)Series
IEEE Signal Processing Society ConferencePagination
1 - 7Publisher
Institute of Electrical and Electronics EngineersLocation
Hong Kong, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-12-11End date
2018-12-13ISBN-13
9781538665367Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
WIFS 2018 Proceedings of the 10th IEEE International Workshop on Information Forensics and Security 2018Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC