Securely Compressive Sensing Using Double Random Phase Encoding

Liu, Hong; Liu, Yan Bing; Xu, Guang Xia

doi:10.4028/www.scientific.net/amr.926-930.3554

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…One is the random measurement matrix, which mainly includes Gaussian measurement matrix, Bernoulli measurement matrix, local Fourier measurement matrix, etc. [ 32 , 39 ]. Although these matrices can reconstruct the original signals well, they are difficult to implement in hardware, in practical applications; storing the matrix elements requires a large storage space.…”

Section: Introductionmentioning

confidence: 99%

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

Yang

Ping

Ding

2022

Entropy

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Many image encryption schemes based on compressive sensing have poor reconstructed image quality when the compression ratio is low, as well as difficulty in hardware implementation. To address these problems, we propose an image encryption algorithm based on the mixed chaotic Bernoulli measurement matrix block compressive sensing. A new chaotic measurement matrix was designed using the Chebyshev map and logistic map; the image was compressed in blocks to obtain the measurement values. Still, using the Chebyshev map and logistic map to generate encrypted sequences, the measurement values were encrypted by no repetitive scrambling as well as a two-way diffusion algorithm based on GF(257) for the measurement value matrix. The security of the encryption system was further improved by generating the Secure Hash Algorithm-256 of the original image to calculate the initial values of the chaotic mappings for the encryption process. The scheme uses two one-dimensional maps and is easier to implement in hardware. Simulation and performance analysis showed that the proposed image compression–encryption scheme can improve the peak signal-to-noise ratio of the reconstructed image with a low compression ratio and has good encryption against various attacks.

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Section: Introductionmentioning

confidence: 99%

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

Yang

Ping

Ding

2022

Entropy

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“…To solve this problem and simultaneously realize compression and encryption, Zhou et al [22] proposed a new image compression-encryption hybrid method by using random measurement matrices. Liu et al [23] presented a block compressed sensing (BCS) scheme by using double random phase encoding (DRPE) to enhance the security level and increase the performance of CS. This method is based on random phase encoding in the fractional Fourier domain for each image block.…”

Section: Introductionmentioning

confidence: 99%

Secure Remote Sensing Image Registration Based on Compressed Sensing in Cloud Setting

Wang

et al. 2019

IEEE Access

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High-resolution remote sensing image registration has recently been widely applied in many fields such as agriculture, forestry, and urban planning. A high-resolution remote sensing image can depict the texture features of ground details more clearly, but it also brings new challenges to registration such as texture similarity, image storage space, and image information leakage. In this paper, to solve the above problems, we propose a finite-state chaotic compressed sensing (CS) cloud remote sensing image registration method. First, chaotic CS with a finite state not only saves storage space required for an image but also improves the security of the image in the transmission process. Next, we process the image in the cloud platform, improve the speed of image processing, and facilitate real-time data processing. Finally, we propose an improved scale invariant feature transform (SIFT) image registration method based on local and global information (LG-SIFT) that reduces the impact of texture similarity on high-resolution remote sensing images. The experimental results show that the runtime of the original processing method is twice as long as that of the proposed scheme and that the accuracy of registration improves considerably. INDEX TERMS High-resolution remote sensing image, compressed sensing, finite-state tent chaos, global context, image registration.

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“…Recently, some researchers proposed a kind of compression-combined encryption method based on compressive sensing (CS) [21][22][23][24][25][26][27]. CS includes sparse representation, linear measurement, and reconstruction processes.…”

Section: Introductionmentioning

confidence: 99%

“…The major core of the encryption system is that two circular matrices and the measurement matrix utilized in compressive sensing are designed by using a two-dimensional Sine Logistic modulation map. Liu et al [25] present a combined compressive sensing and optical image encryption method using double random phase encoding-based block compressive sensing. These CSbased image encryption methods could achieve satisfactory security performance except for the resistance of differentia attack due to the absence of diffusion process.…”

Section: Introductionmentioning

confidence: 99%

A Novel 1D Hybrid Chaotic Map-Based Image Compression and Encryption Using Compressed Sensing and Fibonacci-Lucas Transform

Zhang

et al. 2016

Mathematical Problems in Engineering

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A one-dimensional (1D) hybrid chaotic system is constructed by three different 1D chaotic maps in parallel-then-cascade fashion. The proposed chaotic map has larger key space and exhibits better uniform distribution property in some parametric range compared with existing 1D chaotic map. Meanwhile, with the combination of compressive sensing (CS) and Fibonacci-Lucas transform (FLT), a novel image compression and encryption scheme is proposed with the advantages of the 1D hybrid chaotic map. The whole encryption procedure includes compression by compressed sensing (CS), scrambling with FLT, and diffusion after linear scaling. Bernoulli measurement matrix in CS is generated by the proposed 1D hybrid chaotic map due to its excellent uniform distribution. To enhance the security and complexity, transform kernel of FLT varies in each permutation round according to the generated chaotic sequences. Further, the key streams used in the diffusion process depend on the chaotic map as well as plain image, which could resist chosen plaintext attack (CPA). Experimental results and security analyses demonstrate the validity of our scheme in terms of high security and robustness against noise attack and cropping attack.

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Securely Compressive Sensing Using Double Random Phase Encoding

Cited by 3 publications

References 10 publications

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

Secure Remote Sensing Image Registration Based on Compressed Sensing in Cloud Setting

A Novel 1D Hybrid Chaotic Map-Based Image Compression and Encryption Using Compressed Sensing and Fibonacci-Lucas Transform

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