An Image Encryption Algorithm Based on Compressive Sensing and M Sequence

Dou, Yuqiang; Li, Ming

doi:10.1109/access.2020.3043240

Cited by 13 publications

(7 citation statements)

References 58 publications

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“…So far, many research results exist that are based on chaotic image encryption, such as the Hopfield chaotic neural network [ 6 , 7 , 8 ], which is a typical dynamic neural network with rich dynamic properties; however, the self-feedback Hopfield network used to generate chaotic phenomena is complex in its structure, computationally intensive with fixed parameters, DNA encryption [ 9 , 10 , 11 , 12 ], DNA computation with huge parallelism, and has huge storage and ultra-low power consumption. The compressed sensing (CS) [ 13 , 14 , 15 , 16 ] compression feature allows multimedia encryption schemes with a much reduced length of ciphertext, and simple linear measurements make the encryption process very efficient. Therefore, CS-based image encryption schemes have also attracted a lot of attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Plaintext-Related Dynamic Key Chaotic Image Encryption Algorithm

Ping

Sun

et al. 2021

Entropy

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To address the problems of the high complexity and low security of the existing image encryption algorithms, this paper proposes a dynamic key chaotic image encryption algorithm with low complexity and high security associated with plaintext. Firstly, the RGB components of the color image are read, and the RGB components are normalized to obtain the key that is closely related to the plaintext, and then the Arnold transform is used to stretch and fold the RGB components of the color image to change the position of the pixel points in space, so as to destroy the correlation between the adjacent pixel points of the image. Next, the generated sequences are independently encrypted with the Arnold-transformed RGB matrix. Finally, the three encrypted images are combined to obtain the final encrypted image. Since the key acquisition of this encryption algorithm is related to the plaintext, it is possible to achieve one key per image, so the key acquisition is dynamic. This encryption algorithm introduces chaotic mapping, so that the key space size is 10180. The key acquisition is closely related to the plaintext, which makes the ciphertext more random and resistant to differential attacks, and ensures that the ciphertext is more secure after encryption. The experiments show that the algorithm can encrypt the image effectively and can resist attack on the encrypted image.

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Section: Introductionmentioning

confidence: 99%

Plaintext-Related Dynamic Key Chaotic Image Encryption Algorithm

Ping

Sun

et al. 2021

Entropy

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“…This not only protects the security of information but saves bandwidth, time, and storage space. There are both one-dimensional (1D) [33][34][35][36] and two-dimensional (2D) compressed sensing [37], and encryption schemes for both single [33,38] and multiple images [39,40]. 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.…”

Section: Introductionmentioning

confidence: 99%

“…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. However, many encryption schemes are generated based on traditional compressed sensing theory, using one sampling rate for the whole image and a single measurement matrix to measure it, which is neither efficient nor safe enough [33][34][35][36]38,42]. Also, when the compression ratio is low, the reconstructed image after decryption has a poor visual effect.…”

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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“…Encryption schemes based on chaos and CS are proposed [9][10][11]. Y. Dou and M. Li, [9] proposed an image encryption algorithm based on CS and M sequence. With the help of an improved 1D chaotic system, in the generation of measurement matrix, the computational complexity and storage space are reduced.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach based on Compressive Sensing and Fractional Wavelet Transform for Secure Image Transmission

Chatamoni¹,

Bhukya²,

Jeripotula³

2021

IJIES

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In this paper, a novel scheme for secure image transmission based on compressive sensing (CS) and Fractional Wavelet Transform (FrWT) is proposed. The scheme uses CS and a multi chaotic pseudo random Sine-Tent-Hénon (STH) map based measurement matrix, in the first stage of encryption. Therefore, it provides a simultaneous compression and encryption. Then the security is further enhanced through the novel approach in the second stage of encryption with an iterative procedure, in which a combination of FrWT and random pixel exchange method is applied. The key parameters used in the generation of random matrix, measurement matrix and fractional orders of FrWT are served as keys. This approach has the larger key parameters provided by the STH map and high degree of scrambling and diffusion with FrWT. With the simulation results, the novel proposed scheme has better compression performance as the PSNR value is 35.6631 dB with 0.75 compression ratio and above 25dB with only 4950 coefficients in the reconstruction of image. When the different types of attacks applied, the value of PSNR in the range of 20-30dB shows the good reconstruction robustness of the proposed scheme. Numerical value comparison with other recent CS based encryption schemes, represents the superiority of the proposed scheme in terms of security.

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An Image Encryption Algorithm Based on Compressive Sensing and M Sequence

Cited by 13 publications

References 58 publications

Plaintext-Related Dynamic Key Chaotic Image Encryption Algorithm

Plaintext-Related Dynamic Key Chaotic Image Encryption Algorithm

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

A Novel Approach based on Compressive Sensing and Fractional Wavelet Transform for Secure Image Transmission

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