Coverless Image Steganography Method Based on Feature Selection

Qiu, Anqi; Chen, Xianyi; Sun, Xingming; Wang, Shuai; Guo, Wei

doi:10.32604/jihpp.2019.05881

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Similar coverless steganography is proposed where the Local Binary Patterns (LBP) features of the cover image and the secret images are hashed firstly. Later, the hashes are matched to create the stego image [19]. Similarly, instead of LBP, the edges of the color cover images are obtained.…”

Section: A Traditional-based Steganography Methodsmentioning

confidence: 99%

Image Steganography: A Review of the Recent Advances

et al. 2021

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Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories -traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.

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Section: A Traditional-based Steganography Methodsmentioning

confidence: 99%

Image Steganography: A Review of the Recent Advances

et al. 2021

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“…Information hiding on quantum images with modification of the direction technique [9], [10], local binary patterns [11] are some of the other techniques used. Most of the traditional methods can handle only text as the secret message with reduced hiding capacity.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Image Steganography Using Deep Convolutional Autoencoders

Subramanian

Cheheb²,

Elharrouss

et al. 2021

IEEE Access

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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 lightweight 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.

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