End-to-End Image Steganography Using Deep Convolutional Autoencoders

Subramanian, Nandhini, Cheheb, Ismahane, Elharrouss, Omar, Al-Maadeed, Somaya and Bouridane, Ahmed (2021) End-to-End Image Steganography Using Deep Convolutional Autoencoders. IEEE Access, 9. pp. 135585-135593. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2021.3113953


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: Article
Additional Information: Funding information: This work was made possible by NPRP11S-0113-180276 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the author. Open Access funding was provided by the Qatar National Library.
Uncontrolled Keywords: Image steganography, deep learning, autoencoder, information hiding
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 21 Oct 2021 11:08
Last Modified: 21 Oct 2021 11:15
URI: http://nrl.northumbria.ac.uk/id/eprint/47532

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