Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture Features

Liao, Xin; Yin, Jiaojiao; Chen, Mingliang; Qin, Zheng

doi:10.1109/tdsc.2020.3004708

Cited by 158 publications

(65 citation statements)

References 47 publications

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“…This explains the emergence of embedding strategies of payload distribution in multiple images by fusing multiple features to describe image complexity [14]. Other recent strategies are based on the image texture complexity and the distortion distribution as indicator for secure capacity of each cover image [34]. These strategies are applied on single image steganographic algorithms and experiments shown better resistance to modern universal pooled steganalysis compared to existing methods.…”

Section: Distributed Steganographymentioning

confidence: 99%

Distributed data hiding in multi-cloud storage environment

Metcheka

Ndoundam

2020

J Cloud Comp

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Classical or traditional steganography aims at hiding a secret in cover media such as text, image, audio, video or even in network protocols. Recent research has improved this approach called distributed steganography by fragmenting the secret message and embedding each secret piece into a distinct cover media. The major interest of this approach is to make the secret message detection extremely difficult. However, these file modifications leave fingerprints which can reveal a secret channel to an attacker. Our contribution is a new steganography paradigm transparent to any attacker and resistant to the detection and the secret extraction. Two properties contribute to achieve these goals: the files do not undergo any modification while the distribution of the secret in the multi-cloud storage environment allows us to hide the existence of the covert channel between the communicating parties. Information’s are usually hidden inside the cover media. In this work, the covert media is a pointer to information. Therefore the file carries the information without being modified and the only way to access it is to have the key. Experiments show interesting comparison results with remarkable security contributions. The work can be seen as a new open direction for further research in the field.

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Section: Distributed Steganographymentioning

confidence: 99%

Distributed data hiding in multi-cloud storage environment

Metcheka

Ndoundam

2020

J Cloud Comp

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“…Traditional image steganography algorithms require manual design of the steganography strategy and require sufficient expertise of the designer. As the image is modified, it will inevitably leave modification traces on the image and cause some statistical features of the image to change [4][5][6], increasing the possibility of secret communication exposure. With the development of deep learning techniques, people started to use deep neural networks to minimize the loss between cover image and stego image and use a large amount of data to automate the process of finding a suitable steganography strategy.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Yang

Wang

et al. 2021

Security and Communication Networks

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Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

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“…For a color image, it has three elements of R, G, and B, so it contains more information than grayscale images, its original features, large amount of data, high redundancy, high correlation between pixels. In order to protect image information, major contributions have been made in the fields of steganography 1 , 2 , and encryption 3 – 5 . In recent years, chaos has some ideal cryptographic characteristics such as initial value sensitivity and pseudo-randomness, which makes the chaotic encryption scheme widely used 6 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Color image encryption based on chaotic compressed sensing and two-dimensional fractional Fourier transform

Wang

2020

Sci Rep

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Combining the advantages of structured random measurement matrix and chaotic structure, this paper introduces a color image encryption algorithm based on a structural chaotic measurement matrix and random phase mask. The Chebyshev chaotic sequence is used in the algorithm to generate the flip permutation matrix, the sampling subset and the chaotic cyclic matrix for constructing the structure perceptual matrix and the random phase mask. The original image is compressed and encrypted simultaneously by compressed sensing, and re-encrypted by two-dimensional fractional Fourier transform. Simulation experiments show the effectiveness and reliability of the algorithm.

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Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture Features

Cited by 158 publications

References 47 publications

Distributed data hiding in multi-cloud storage environment

Distributed data hiding in multi-cloud storage environment

High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Color image encryption based on chaotic compressed sensing and two-dimensional fractional Fourier transform

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