Improved image steganography based on super-pixel and coefficient-plane-selection

Kadhim, Inas Jawad; Premaratne, Prashan; Vial, Peter James

doi:10.1016/j.sigpro.2020.107481

Cited by 24 publications

(11 citation statements)

References 39 publications

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“…In the above formula, N and M, respectively, represent the dimension rearrangement of the column vectors by the element values of high-and low-resolution feature blocks of film, television, and animation video images, and 1/N and 1/M are the cost between D h and D 1 of balanced formula (9). In order to facilitate subsequent calculation, formula (10) is reconstituted:…”

Section: Image Reconstruction Methods Of Multiframe Animationmentioning

confidence: 99%

“…After determining the triangular meshes on a two-dimensional plane through screen projection, a continuous scaling field shall be assigned to each triangular facet; that is, a scaling factor shall be assigned to each triangular facet, and the scaling factor between adjacent triangular facets shall be continuously variable. e specific process is as follows [9,10]:…”

Section: 2mentioning

confidence: 99%

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Animation Design Based on 3D Visual Communication Technology

Shan

Wang

2022

Scientific Programming

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The depth synthesis of image texture is neglected in the current image visual communication technology, which leads to the poor visual effect. Therefore, the design method of film and TV animation based on 3D visual communication technology is proposed. Collect film and television animation videos through 3D visual communication content production, server processing, and client processing. Through stitching, projection mapping, and animation video image frame texture synthesis, 3D vision conveys animation video image projection. In order to ensure the continuous variation of scaling factors between adjacent triangles of animation and video images, the scaling factor field is constructed. Deep learning is used to extract the deep features and to reconstruct the multiframe animated and animated video images based on visual communication. Based on this, the frame feature of video image under gray projection is identified and extracted, and the animation design based on 3D visual communication technology is completed. Experimental results show that the proposed method can enhance the visual transmission of animation video images significantly and can achieve high-precision reconstruction of video images in a short time.

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Section: Image Reconstruction Methods Of Multiframe Animationmentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

Animation Design Based on 3D Visual Communication Technology

Shan

Wang

2022

Scientific Programming

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“…Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. In coverless image steganography [41,42], deep learning is applied to extract highdimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence.…”

Section: Fig 2 General Model Of Information Extraction and Original Media Recoverymentioning

confidence: 99%

“…Recently, some researchers have applied neural networks to steganography [35][36][37][38][39][40][41][42][43][44][45]. As a cooperative algorithm, convolutional neural network with deep supervision edge detector retains more edge pixels over conventional edge detection, so it can increase data hiding capacity [35].…”

Section: Fig 1 a Simple Model Of Information Hidingmentioning

confidence: 99%

Large-Capacity Image Data Hiding based on Table Look-up

Ding¹,

Zhang²,

Reulke³

et al. 2021

Preprint

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In previous data hiding techniques, binary rules are usually used to guide the fine-tuning of the values of basic objects in the host media to hide bit 0 and bit 1. In this paper, we propose a new data hiding technique for gray images based on querying a 256x256 information table. The information table is constructed by cloning a 3x3 basic block, which we call seed block. Eight unsigned integer values between 0 and 7, i.e., 3 bit binary data, are assigned to different elements of the seed block. Each time, a pair of pixels are chosen from a host image, and their pixel values are used as row and column numbers to look up the information table. If element value obtained by looking up the table is equal to the 3 bit binary data to be hidden, the values of the pixel pair will remain unchanged. Otherwise, take this element as the central point, we call it the focus element, to enclose a 3x3 window in the information table. Then in the window, find the element which is equal to the data to be hidden. Finally, update the pixel values of the pair with the row and column numbers of the found element in the window. Since the row and column numbers are in the range of 0-255, the updated pixel values will not overflow. In the proposed algorithm, a pair of pixels can hide 3 bits of information, so the embedding capacity is very high. Since the adjustment of pixel values is constrained in a 3x3 window, the modification amount of pixel values is small. The proposed technique belongs to fragile digital watermarking, so it can be used for image authentication and tamper localization. By the evaluation of data hiding capacity, security, imperceptibility, computational cost and extensibility, this algorithm is superior to existing information hiding techniques. The proposed technique can also be used in color image and audio data hiding.

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“…Different techniques such as watermarking (Hosny et al 2018;Hosny et al 2019;Hosny et al 2021a, b), image steganography (Zhou et al 2019;Kadhim et al 2020), and image encryption (Naskar et al 2020) are frequently used for securing digital images. Image encryption technique is based on two main stages: encryption and decryption.…”

Section: Introductionmentioning

confidence: 99%

Novel encryption for color images using fractional-order hyperchaotic system

Hosny

Kamal

Darwish

2022

J Ambient Intell Human Comput

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The fractional-order functions show better performance than their corresponding integer-order functions in various image processing applications. In this paper, the authors propose a novel utilization of fractional-order chaotic systems in color image encryption. The 4D hyperchaotic Chen system of fractional-order combined with the Fibonacci Q-matrix. The proposed encryption algorithm consists of three steps: in step#1, the input image decomposed into the primary color channels, R, G, & B. The confusion and diffusion operations are performed for each channel independently. In step#2, the 4D hyperchaotic Chen system of fractional orders generates random numbers to permit pixel positions. In step#3, we split the permitted image into 2 × 2 blocks where the Fibonacci Q-matrix diffused each of them. Experiments performed where the obtained results ensure the efficiency of the proposed encryption algorithm and its ability to resist attacks.

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Improved image steganography based on super-pixel and coefficient-plane-selection

Cited by 24 publications

References 39 publications

Animation Design Based on 3D Visual Communication Technology

Animation Design Based on 3D Visual Communication Technology

Large-Capacity Image Data Hiding based on Table Look-up

Novel encryption for color images using fractional-order hyperchaotic system

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