Image encryption using sparse coding and compressive sensing

Ponuma, R.; Amutha, R.

doi:10.1007/s11045-019-00634-x

Cited by 19 publications

(5 citation statements)

References 26 publications

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“…Here, we compare the proposed measurement matrix with the partial Hadamard matrix and the Gaussian random matrix. Partial Hadamard matrix, a famous measurement matrix, has been used in encryption schemes 22,35,38,39,45 . Table 4 shows the effectiveness of these measurement matrices in the proposed encryption process for the different CRs.…”

Section: Resultsmentioning

confidence: 99%

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Image compression-encryption method based on two-dimensional sparse recovery and chaotic system

Ghaffari

2021

Sci Rep

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In this paper, we propose an image compression-encryption method based on two-dimensional (2D) sparse representation and chaotic system. In the first step of this method, the input image is extended in a transform domain to obtain a sparse representation. To achieve better performance of image compression by 2D sparse recovery, the sparse representation is scrambled via a chaotic confusion. This step helps the satisfaction of the uniqueness conditions for sparse recovery, and the security level of encryption is increased. Then, two orthogonal measurement matrices are generated using the chaotic time series. The singular value decomposition is used to compress the sparse scrambled representation in two dimensions. Finally, to reduce the correlation between adjacent pixels in the compressed matrix, and obtain a uniform distribution in the encrypted image, a compressed scrambling matrix based on chaotic confusion is used. Then, XOR operation is applied for final encryption. In the decryption process, to improve the compression efficiency, the total variation constraint is added to the 2D sparse recovery problem based on the smoothed norm. The simulation results demonstrate the satisfying performance of the proposed method for different compression ratios. Security analysis describes the effectiveness of the proposed encryption approach.

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Section: Resultsmentioning

confidence: 99%

“…In sparse recovery, the transform which is used to obtain sparse representation has a critical role. Many different transforms, such as wavelet 34 , 40 , DCT 38 , and learned dictionary 35 , were used in different approaches. The transforms also can be categorized into block-based transform 35 and global-based transform 21 , 34 .…”

Section: Introductionmentioning

confidence: 99%

Image compression-encryption method based on two-dimensional sparse recovery and chaotic system

Ghaffari

2021

Sci Rep

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“…Luo et al [125] designed compression and encryption strategy for images based on compressive sensing and Haar wavelet. Ponuma and Amutha [126] utilized sparse coding and compressive sensing to encrypt the images. Sparse coding is utilized to discover the sparse representation of images as a straight combination of iotas from an overcomplete fixed dictionary.…”

Section: Compressive Sensing-based Image Encryptionmentioning

confidence: 99%

Computational Image Encryption Techniques: A Comprehensive Review

Kaur

Singh

Kaur

2021

Mathematical Problems in Engineering

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Images contain very sensitive and confidential information. Because images play a significant role in many applications such as military communication, remote-sensing, and medical-imaging, therefore, it is necessary to protect sensitive and confidential information from unauthorized use and modification. To achieve this objective, encryption is one of the best methods among the information hiding methods. In recent years, many image encryption approaches are designed by the researchers. They use different concepts for image encryption to increase security. The main aim of this paper is to present a comprehensive review of the existing image encryption approaches. These approaches are categorized based on different concepts such as chaotic maps, DNA, compressive sensing, and optical image encryption. Comparisons are made among the existing approaches to access the various security parameters. Key performance metrics are also presented. The future scope of image encryption is also presented to encourage the research community.

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“…So, it is widely used in image encryption. [21][22][23][24][25][26][27] Yu et al 21 proved that the measurement matrix generated by chaotic sequences can meet the restricted isometry property (RIP) property, of which provides a new way for constructing the measurement matrix. Based on it, Zhou et al 22 proposed a construction method for measurement matrix by circular matrix using a logistic map.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, the image can be simultaneously compressed and encrypted during sampling. So, it is widely used in image encryption 21‐27 . Yu et al 21 proved that the measurement matrix generated by chaotic sequences can meet the restricted isometry property (RIP) property, of which provides a new way for constructing the measurement matrix.…”

Section: Introductionmentioning

confidence: 99%

A novel multi‐image visually meaningful encryption algorithm based on compressive sensing and Schur decomposition

Pan

Dong

et al. 2020

Trans Emerging Tel Tech

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Image encryption algorithm is an important telecommunication technology for protecting personal security. In this article, we propose a novel multi‐image visual encryption algorithm based on compressive sensing (CS) and Schur decomposition. First, multiple images are sparsed by a discrete wavelet transform, and the compressed images are obtained by zigzag confusion and CS. Second, these compressed images are combined according to random sequences and scrambled again to obtain the secret image. Third, the carrier image is subjected to a two‐level lifting wavelet transform where the low‐frequency and high‐frequency subbands are selected to perform a discrete cosine transform (DCT). Fourth, Schur decomposition is employed on the secret image to calculate an upper triangular matrix and a unitary matrix (orthogonal matrix) to be embedded into the DCT coefficients of the previous low‐frequency and high‐frequency subbands, respectively. With an inverse transformation, the final visually meaningful encrypted image is achieved. Here, the initial position of the zigzag confusion is related to the plain image, which can effectively resist known‐plaintext and chosen‐plaintext attacks. The simulation results demonstrate that the proposed algorithm has high security and good imperceptibility.

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Image encryption using sparse coding and compressive sensing

Cited by 19 publications

References 26 publications

Image compression-encryption method based on two-dimensional sparse recovery and chaotic system

Image compression-encryption method based on two-dimensional sparse recovery and chaotic system

Computational Image Encryption Techniques: A Comprehensive Review

A novel multi‐image visually meaningful encryption algorithm based on compressive sensing and Schur decomposition

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