Color image encryption algorithm based on DNA code and alternating quantum random walk

Wang, Yinuo; Song, Zhaoyang; Ma, Yun; Hua, Nan; Ma, Hongyang

doi:10.7498/aps.70.20211255

Cited by 20 publications

(11 citation statements)

References 43 publications

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“…According to the results, the VGG19 model achieved unquestionably improved accuracy of 90.48% while the NB method demonstrated the least accuracy of 88.94%. Subsequently, the BoVWbased SVM, CNN, AlexNet, and ResNet34 models have reached closer accu y of 95.59%, 96.93%, 94.82%, and 96.78%, respectively [28]. However, the BTDC-MOML model has exhibited maximum accu racy of 95.71%.…”

Section: Experimental Validationsmentioning

confidence: 95%

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Uvaneshwari¹,

Baskar²

2023

Computer Systems Science and Engineering

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Section: Experimental Validationsmentioning

confidence: 95%

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Uvaneshwari¹,

Baskar²

2023

Computer Systems Science and Engineering

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“…Obviously, logical operators span two boundaries of the same type. The codeword length 30 is determined as the product of the side lengths, expressed in the number of qubits, and for the surface code in Figure , we have N = L X ⋅ L Z = 5 ⋅ 5 = 25.…”

Section: Surface Codementioning

confidence: 99%

Multidimensional Bose quantum error correction based on neural network decoder

Wang

Xue

et al. 2022

npj Quantum Inf

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Boson quantum error correction is an important means to realize quantum error correction information processing. In this paper, we consider the connection of a single-mode Gottesman-Kitaev-Preskill (GKP) code with a two-dimensional (2D) surface (surface-GKP code) on a triangular quadrilateral lattice. On the one hand, we use a Steane-type scheme with maximum likelihood estimation for surface-GKP code error correction. On the other hand, the minimum-weight perfect matching (MWPM) algorithm is used to decode surface-GKP codes. In the case where only the data GKP qubits are noisy, the threshold reaches σ ≈ 0.5 ($$\bar{p}\approx 12.3 \%$$ p ¯ ≈ 12.3 % ). If the measurement is also noisy, the threshold is reached σ ≈ 0.25 ($$\bar{p}\approx 10.02 \%$$ p ¯ ≈ 10.02 % ). More importantly, we introduce a neural network decoder. When the measurements in GKP error correction are noise-free, the threshold reaches σ ≈ 0.78 ($$\bar{p}\approx 15.12 \%$$ p ¯ ≈ 15.12 % ). The threshold reaches σ ≈ 0.34 ($$\bar{p}\approx 11.37 \%$$ p ¯ ≈ 11.37 % ) when all measurements are noisy. Through the above optimization method, multi-party quantum error correction will achieve a better guarantee effect in fault-tolerant quantum computing.

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“…At present, protecting the security of image information is one of the research hotspots at home and abroad. Te methods of protecting the secret image of an image are roughly divided into two categories: image watermarking [1][2][3][4][5] and image encryption [6][7][8][9]. Image watermarking is to embed the pixel data of the watermark image into the carrier image in a certain way, and there is no visual diference between the carrier image and the watermarked image, thus achieving the efect of hiding the watermark image.…”

Section: Introductionmentioning

confidence: 99%

A Quantum Image Watermarking Scheme Based on Quantum Hilbert Scrambling and Steganography about the Moiré Fringe

Jiang

Zhang

et al. 2023

Quantum Engineering

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In order to boost the security and confidentiality of information in quantum images, on the foundation of the NEQR model, a novel quantum watermarking scheme combining quantum Hilbert scrambling with steganography based on the Moiré fringe is designed in this paper. First of all, for carrier image, and watermark image, the color information and position information are denoted, respectively, by the NEQR model. Next, the watermark image is converted to a disordered image by quantum Hilbert scrambling, and the message of the original watermark image cannot be gained from the disordered image. At last, the watermark image after scrambling is embedded into the carrier image through the steganography of the Moiré fringe, obtaining the watermarked image. Due to the unitary image of the quantum gate, quantum Hilbert inverse scrambling is the opposite process of quantum Hilbert scrambling. In addition, the watermark image can be completely extracted from the watermarked image. What’s more, the experimental simulation and performance analysis of the scheme are done. The experimental simulation proves the feasibility of this algorithm. Visually, there is no difference between the carrier image and the watermarked image. The PSNR between the watermarked image and the carrier image is measured, which quantitatively shows the high similarity. In addition, the time complexity of the quantum circuit is lower than some other quantum image watermarking schemes, which proves the simplicity of this scheme.

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Color image encryption algorithm based on DNA code and alternating quantum random walk

Cited by 20 publications

References 43 publications

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Multidimensional Bose quantum error correction based on neural network decoder

A Quantum Image Watermarking Scheme Based on Quantum Hilbert Scrambling and Steganography about the Moiré Fringe

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