Image Steganography Based on Cellular Automata

Jana, Biswapati; Giri, Debasis; Mondal, Shymal Kumar; Pal, Pabitra

doi:10.12732/ijpam.v83i5.14

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…e training process of GAN can be summarized as a Minimax game: discriminator D tries to maximize the probability of correctly distinguishing real samples from generated samples; generator model G tries to maximize the probability that discriminator model D cannot distinguish generated samples. e objective function when training GAN is shown in (1), where G (z) represents the sample generated by generator G according to input random noise vector z, and D (x) represents the probability that discriminator model D determines that sample x is the real sample:…”

Section: Gan and Itsmentioning

confidence: 99%

“…Data hiding is an important technique to solve security problems and protect data. Steganography is an important branch of data hiding, which can be divided into image steganography [1,2], audio steganography [3,4], and video steganography [5,6], according to different carriers. Image steganography aims to conceal secret data within cover images transmitted through public channels without causing suspicion [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

Wang

Song

et al. 2021

Security and Communication Networks

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The deep learning based image steganalysis is becoming a serious threat to modification-based image steganography in recent years. Generation-based steganography directly produces stego images with secret data and can resist the advanced steganalysis algorithms. This paper proposes a novel generation-based steganography method by disguising the stego images into the kinds of images processed by normal operations (e.g., histogram equalization and sharpening). Firstly, an image processing model is trained using DCGAN and WGAN-GP, which is used to generate the images processed by normal operations. Then, the noise mapped by secret data is inputted into the trained model, and the obtained stego image is indistinguishable from the processed image. In this way, the steganographic process can be covered by the process of image processing, leaving little embedding trace in the process of steganography. As a result, the security of steganography is guaranteed. Experimental results show that the proposed scheme has better security performance than the existing steganographic methods when checked by state-of-the-art steganalytic tools, and the superiority and applicability of the proposed work are shown.

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Section: Gan and Itsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

Wang

Song

et al. 2021

Security and Communication Networks

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“…La Tabla 1 muestra los valores de PSNR obtenidos para cada imagen en las diferentes versiones del algoritmo En otras investigaciones [10][11][12] obtienen valores de PSNR mayores a los reportados, pero es muy probable que sea por las dimensiones de imágenes que se utilizan. En estas investigaciones la imagen portadora tiene una capacidad en bits mayor que la mínima requerida para ocultar la imagen secreta.…”

Section: Psnrunclassified

Algoritmo esteganográfico basado en autómatas celulares y en la sustitución de los bits menos significativos

López¹,

D'Ávila²,

Zapata³

et al. 2019

RCS

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Resumen. En esta investigación se presenta un nuevo algoritmo de esteganografía para imágenes, utilizando la sustitución de los bits menos significativo y la regla local 90 de autómatas celulares. Este algoritmo es capaz de cifrar y ocultar los pixeles de la imagen en un proceso combinado. El factor PSNR indica que no existe una distorsión severa en la imagen esteganográfica, lo que en esta área se interpreta como a menor distorsión de la imagen esteganográfica, mayor es la probabilidad de pasar desapercibida y que no sea analizada. Sin embargo, en caso de que la imagen oculta sea extraída, ésta se encuentra protegida por una clave secreta. Este algoritmo transforma la imagen secreta a una imagen cifrada con distribución uniforme y sin correlación. Este algoritmo esteganográfico puede ser una nueva opción para ocultar y proteger imágenes relacionando ambos procesos.

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“…us, N different dense images are generated. en, by transporting the secret image, the purpose of safe transport of the secret image is completed [8].…”

Section: Introductionmentioning

confidence: 99%

Visual UI Design Image Sharing Scheme Based on Improved FEMD Algorithm

Shen

Dong

2022

Mobile Information Systems

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Based on the improvement of the FEMD algorithm, this article assumes a reversible sharing scheme that can encrypt images. UI design is the most important part of Internet applications, including three parts, namely, interaction design, interface design, and user research. Therefore, the UI directly affects the user experience. It is precisely based on the criticality and importance of the UI algorithm that we intend to further explore the most critical expression language in the product and to innovate the most important visual factors by further digging into the user’s demands for the product. The three most important colors in the UI design process are colors, pictures, and text. And interaction design is the most important and critical part of UI design. We need to explore the dynamic relationship among applications, humans, and machines. For visual design, we not only need to deal with the relationship between application, human, and machine, but also better deal with the relationship between plane, time, and space on the basis of these three relationships. Our innovative interface for the UI algorithm is mainly to innovate the first interface that the user obtains, so that the user can get a better reading experience and browse, and the designer can beautify and collect the user interface to achieve the user the purpose of obtaining a better browsing sensory experience. This requires us to get rid of the old copy model, in order to innovate in thinking; innovate a better visual communication interface, and better integrate the user’s research interface design and interaction, which is also the pursuit of this article.

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Image Steganography Based on Cellular Automata

Cited by 6 publications

References 9 publications

Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

Algoritmo esteganográfico basado en autómatas celulares y en la sustitución de los bits menos significativos

Visual UI Design Image Sharing Scheme Based on Improved FEMD Algorithm

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