Image encryption scheme based on alternate quantum walks and discrete cosine transform

Ma, Yun; Li, Nachuan; Zhang, Wenbin; Wang, Shumei; Ma, Hongyang

doi:10.1364/oe.431945

Cited by 34 publications

(22 citation statements)

References 39 publications

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“…Image encryption algorithms will destroy this correlation, and the degree of destruction can reflect the effect of encryption algorithms. The closer R AB is to 0 , the better the destruction effect is, and the more difficult it is to obtain image information through the relationship between adjacent pixels [27].…”

Section: Correlation Analysismentioning

confidence: 99%

“…yang et al [25] studied the properties of one-dimensional QW and applied it to quantum image encryption for the first time. Abd et al [26] analyzed the statistical properties of the probability distribution matrix of twodimensional quantum walks and applied it to image encryption; Ma et al [27] combined the discrete cosine transform (DCT) [28] and the probability matrix of alternating quantum walks (AQW) for image encryption, etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coupling Quantum Random Walks with Long and Short Term Memory for High Pixel Image Encryption Schemes

Liang¹,

Song²,

Sun³

et al. 2022

Preprint

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This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the Long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029% and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.

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Section: Correlation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coupling Quantum Random Walks with Long and Short Term Memory for High Pixel Image Encryption Schemes

Liang¹,

Song²,

Sun³

et al. 2022

Preprint

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show abstract

“…Quantum mechanics is a set of rules constructed by mathematical framework or physical theory. As a new computational framework for integrating the concepts of quantum mechanics, quantum computing [1,2] utilizes the properties of quantum mechanics to process both quantum and classical information [3,4], which may cause an insurmountable gap between quantum and classical computers. As quantum technology is developing, noisy intermediate-scale quantum computers (NISQ) have begun to deal with some relatively complex computing tasks, and their computing power has surpassed classical computers in some tasks [5].…”

Section: Introductionmentioning

confidence: 99%

An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

Chu

Liu

et al. 2022

Quantum Engineering

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Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.

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“…As one of the important carriers in the process of information transmission, digital images play a pivotal role in many fields, such as education, finance, medical treatment and so on. However, digital images are easily intercepted and tampered during transmission, which greatly threatens the privacy of image information 1 – 4 . In view of the security of digital images, many domestic and foreign researchers have brought forward various image encryption methods.…”

Section: Introductionmentioning

confidence: 99%

Color image encryption scheme based on alternate quantum walk and controlled Rubik’s Cube

Zhao

Zhang

Jiang

et al. 2022

Sci Rep

Self Cite

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Aiming at solving the trouble that digital image information is easily intercepted and tampered during transmission, we proposed a color image encryption scheme based on alternate quantum random walk and controlled Rubik’s Cube transformation. At the first, the color image is separated into three channels: channel R, channel G and channel B. Besides, a random sequence is generated by alternate quantum walk. Then the six faces of the Rubik’s Cube are decomposed and arranged in a specific order on a two-dimensional plane, and each pixel of the image is randomly mapped to the Rubik’s Cube. The whirling of the Rubik’s Cube is controlled by a random sequence to realize image scrambling and encryption. The scrambled image acquired by Rubik’s Cube whirling and the random sequence received by alternate quantum walk are bitwise-XORed to obtain a single-channel encrypted image. Finally the three-channel image is merged to acquire the final encrypted image. The decryption procedure is the reverse procedure of the encryption procedure. The key space of this scheme is theoretically infinite. After simulation experiments, the information entropy after encryption reaches 7.999, the NPCR is 99.5978%, and the UACI is 33.4317%. The encryption scheme with high robustness and security has a excellent encryption effect which is effective to resist statistical attacks, force attacks, and other differential attacks.

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Image encryption scheme based on alternate quantum walks and discrete cosine transform

Cited by 34 publications

References 39 publications

Coupling Quantum Random Walks with Long and Short Term Memory for High Pixel Image Encryption Schemes

Coupling Quantum Random Walks with Long and Short Term Memory for High Pixel Image Encryption Schemes

An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

Color image encryption scheme based on alternate quantum walk and controlled Rubik’s Cube

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