Subdata image encryption scheme based on compressive sensing and vector quantization

With the advancement of multimedia technology and coming of big data era, the size of image data is significantly increased. However, the traditional image encryption methods cannot solve the emerging problems of efficient compression. To settle with this challenge, an effective content-adaptive image compression and encryption method based on compressive sensing and double random phase encoding (DRPE) is proposed in this paper. The original image is converted to one low-frequency part and three high-frequency parts by DWT and then permutated by sorting-based chaotic sequences. Afterward, a novel measurement matrix optimization algorithm based on adaptive step size is presented to measure the high-frequency components. To enhance the security of the scheme, the DRPE, quantization, and diffusion are successively performed on the complex matrix composed of the shuffled low-frequency component and three measurement value matrices to obtain the cipher image. Logistic-Sine chaotic system is utilized to produce the chaotic keystreams for the encryption process, and its system parameter and initial value are determined by the information entropy of the plain image and external key parameters, so that the proposed cipher can withstand known-plaintext and chosen-plaintext attacks effectively. Numerical experiments demonstrate the effectiveness of the proposed image compression and encryption algorithm.

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“…Assuming that μ all i j (g i j ) 2 is the overall correlation coefficient, and it may be expressed as (11)…”

Section: Overall Correlation Coefficientmentioning

confidence: 99%

“…However, these schemes cannot efficiently compress the images to save the transmission and storage pressures. Compressive sensing (CS) may compress, encrypt, and sample the plain image simultaneously [10][11][12], and some image encryption algorithms based on CS have been proposed accordingly.…”

Section: Introductionmentioning

confidence: 99%

Content-adaptive image compression and encryption via optimized compressive sensing with double random phase encoding driven by chaos

Gan

Chai

et al. 2022

Complex Intell. Syst.

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“…Because the digital image has the characteristics of a considerable amount of information and high data redundancy, the conventional data encryption technology, such as AES, DES, etc, cannot totally satisfy the demands of digital image [3][4][5]. To protect the image content security, lots of image encryption algorithms based on various techniques have been proposed, such as compressed sensing [6][7][8], DNA computing [9][10][11], Latin square [12], S-box [13][14][15], RNA operation [16], etc. Most of image encryption algorithms include the confusion and diffusion stages [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Color image encryption algorithm based on 3D spiral transform and radial diffusion

Zhang¹,

Yang²

2022

Phys. Scr.

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While digital image information is convenient to transmit in Internet, there is a great risk of information leakage in practice. To satisfy the demands for image content security and real-time communication, we propose a color image encryption algorithm based on a three-dimensional (3D) spiral transform and radial diffusion. Firstly, a 3D spiral transform is designed by combining the R, G and B channels of the color image with the traditional spiral transform; secondly, after the randomly selecting the spiral starting point by the chaotic sequence, the plained image is scrambled by the 3D spiral transform; thirdly, after randomly selecting the method of radial diffusion by the chaotic sequence, the scrambled image is radially diffused by chaotic sequences to obtain the cipher image. Simulation results and theoretical analyses show that the proposed algorithm not only has an excellent encryption effect, but also has the strong security to resist common attacks. Thus, the proposed algorithm can be suitable for the color image transmission under the network platform with the high-security requirement.

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“…Chai et al [39] proposed a scheme to compress and encrypt two color images at the same time through parallel operations on their RGB components, which improves efficiency and enhances security. To further reduce the bandwidth and computing load, Fan et al [41] proposed an algorithm combined with vector quantization (VQ) and CS, distinguishing important and secondary information. Important data are extracted by a VQ compression algorithm, and the secondary data are compressed by a CS algorithm, so as to achieve higher compression efficiency.…”

Section: Introductionmentioning

confidence: 99%

Image Encryption Scheme Based on Multiscale Block Compressed Sensing and Markov Model

Shi

Wang

2021

Entropy

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Many image encryption schemes based on compressed sensing have the problem of poor quality of decrypted images. To deal with this problem, this paper develops an image encryption scheme by multiscale block compressed sensing. The image is decomposed by a three-level wavelet transform, and the sampling rates of coefficient matrices at all levels are calculated according to multiscale block compressed sensing theory and the given compression ratio. The first round of permutation is performed on the internal elements of the coefficient matrices at all levels. Then the coefficient matrix is compressed and combined. The second round of permutation is performed on the combined matrix based on the state transition matrix. Independent diffusion and forward-backward diffusion between pixels are used to obtain the final cipher image. Different sampling rates are set by considering the difference of information between an image’s low- and high-frequency parts. So, the reconstruction quality of the decrypted image is better than that of other schemes, which set one sampling rate on an entire image. The proposed scheme takes full advantage of the randomness of the Markov model and shows an excellent encryption effect to resist various attacks.

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Subdata image encryption scheme based on compressive sensing and vector quantization

Cited by 27 publications

References 35 publications

Content-adaptive image compression and encryption via optimized compressive sensing with double random phase encoding driven by chaos

Content-adaptive image compression and encryption via optimized compressive sensing with double random phase encoding driven by chaos

Color image encryption algorithm based on 3D spiral transform and radial diffusion

Image Encryption Scheme Based on Multiscale Block Compressed Sensing and Markov Model

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