End-to-end image steganography using deep convolutional autoencoders

Subramanian, N., Cheheb, I. ORCID: 0000-0002-0961-0476, Elharrouss, O., Al-Maadeed, S. and Bouridane, A., 2021. End-to-end image steganography using deep convolutional autoencoders. IEEE Access, 9, pp. 135585-135593.

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Abstract

Image steganography is used to hide a secret image inside a cover image in plain sight. Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret binary bits. Overloading the cover image may lead to distortions and the secret information may become visible. Hence the hiding capacity of the traditional methods are limited. In this paper, a light-weight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image. The proposed method is evaluated using three datasets - COCO, CelebA and ImageNet. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to measure the performance. The proposed method has been evaluated using various images including Lena, airplane, baboon and peppers and compared against other traditional image steganography methods. The experimental results have demonstrated that the proposed method has higher hiding capacity, security and robustness, and imperceptibility performances than other deep learning image steganography methods.

Item Type: Journal article
Publication Title: IEEE Access
Creators: Subramanian, N., Cheheb, I., Elharrouss, O., Al-Maadeed, S. and Bouridane, A.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 20 September 2021
Volume: 9
Identifiers:
NumberType
10.1109/access.2021.3113953DOI
1534919Other
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 11 Apr 2022 10:41
Last Modified: 11 Apr 2022 10:41
URI: http://irep.ntu.ac.uk/id/eprint/46088

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