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Hiding multiple images into a single image via joint compressive autoencoders

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posted on 2022-06-15, 08:10 authored by Xiyao Liu, Ziping Ma, Zhihong Chen, Fangfang Li, Ming Jiang, Gerald SchaeferGerald Schaefer, Hui FangHui Fang

Interest in image hiding has been continually growing. Recently, deep learningbased image hiding approaches improve the hidden capacity significantly. However, the major challenges of the existing methods are that they are difficult to balance between the errors of the modified cover image and those of the recovered secret image. To solve this problem, in this paper, we develop an image hiding algorithm based on a joint compressive autoencoder framework. Further, we propose a novel strategy to enlarge the hidden capacity, i.e., hiding multi-images in one container image. Specifically, our approach provides an extremely high image hidden capacity coupled with small reconstruction errors of the secret image. More importantly, we tackle the trade-off problem of earlier approaches by mapping the image representations in the latent spaces of the joint compressive autoencoder models, leading to both high visual quality of the container image and low reconstruction error the secret image. In an extensive set of experiments, we confirm our proposed approach to outperform several state-of-the-art image hiding methods, yielding high imperceptibility and steganalysis resistance of the container images with high recovery quality of the secret images, while improving the image hidden capacity significantly (four times higher than full-image hiding capacity).

Funding

National Natural Science Foundation of China (61602527,61772555,61772553)

Natural Science Foundation of Hunan Province, China (2020JJ4746)

Changsha Municipal Natural Science Foundation (kq2014134)

Guangxi Key Laboratory of Trusted Software (KX202032)

History

School

  • Science

Department

  • Computer Science

Published in

Pattern Recognition

Volume

131

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-06-09

Publication date

2022-06-11

Copyright date

2022

ISSN

0031-3203

Language

  • en

Depositor

Dr Hui Fang. Deposit date: 9 June 2022

Article number

108842

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