An Encryption Algorithm for Region of Interest in Medical DICOM Based on One-Dimensional eλ-cos-cot Map

Meng, Xin; Li, Jinqing; Di, Xiaoqiang; Sheng, Yaohui; Jiang, Donghua

doi:10.3390/e24070901

Cited by 9 publications

(4 citation statements)

References 55 publications

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“…12. The encrypted data is considered random data due to the entropy value for encrypted data being very close to eight [30]. So, the proposed technique can withstand entropy attacks, and the possibility of occurrence of this type of attack is negligible.…”

Section: Information Entropy Analysismentioning

confidence: 99%

Partially Deep-Learning Encryption Technique

Mousa¹

2023

Computers, Materials &Amp; Continua

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The biggest problem facing the world is information security in the digital era. Information protection and integrity are hot topics at all times, so many techniques have been introduced to transmit and store data securely. The increase in computing power is increasing the number of security breaches and attacks at a higher rate than before on average. Thus, a number of existing security systems are at risk of hacking. This paper proposes an encryption technique called Partial Deep-Learning Encryption Technique (PD-LET) to achieve data security. PD-LET includes several stages for encoding and decoding digital data. Data preprocessing, convolution layer of standard deep learning algorithm, zigzag transformation, image partitioning, and encryption key are the main stages of PD-LET. Initially, the proposed technique converts digital data into the corresponding matrix and then applies encryption stages to it. The implementation of encrypting stages is frequently changed. This collaboration between deep learning and zigzag transformation techniques provides the best output result and transfers the original data into a completely undefined image which makes the proposed technique efficient and secure via data encryption. Moreover, its implementation phases are continuously changed during the encryption phase, which makes the data encryption technique more immune to some future attacks because breaking this technique needs to know all the information about the encryption technique. The security analysis of the obtained results shows that it is computationally impractical to break the proposed technique due to the large size and diversity of keys and PD-LET has achieved a reliable security system.

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Section: Information Entropy Analysismentioning

confidence: 99%

Partially Deep-Learning Encryption Technique

Mousa¹

2023

Computers, Materials &Amp; Continua

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“…In recent years, cos, sin and other trigonometric maps have been widely used in image encryption algorithms due to their easy implementation [33,34]. However, the existing map methods have disadvantages such as blank windows and uneven distribution of chaotic sequences, which lead to security problems in image encryption schemes.…”

Section: Designed 2d-slc Hyperchaotic Mapmentioning

confidence: 99%

Explore the potential of deep learning and hyperchaotic map in the meaningful visual image encryption scheme

Chen

Wang

Xiao

et al. 2023

IET Image Processing

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In recent years, meaningful visual image encryption schemes that the plain image is compressed and encrypted and then hidden into the carrier image have received increasing attention. This paper proposes a new meaningful visual image encryption scheme, which consists of three stages: compression (compression network)—encryption (2D‐SLC hyperchaotic map)—hiding (matrix encoding). First, the advantages of deep learning are explored. It can compress the width, height, channel, and pixel values of the plain image simultaneously. Second, a new 2D‐SLC hyperchaotic map is designed to ensure security. It has a larger chaotic space and better randomness. Finally, to obtain a high‐quality cipher image, the secure secret image is hidden in the grey carrier image by matrix encoding. The scheme can compress and encrypt the grey or colour plain image and then hide it in a grey carrier image. In addition, the theoretical peak signal‐to‐noise ratio (PSNR) between the cipher image and the carrier image is improved from 40.9292 to 42.1785 dB. The total running time is only about 0.35, 0.87 and 3.1 s for a 256 × 256, 512 × 512 and 1024 × 1024 grey or colour plain image, respectively.

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“…W. El-Shafai et al [14] developed an algorithm for direct encryption of medical image pixels using logistic maps, piecewise linear chaotic map (PWLCM), and de-oxyribo nucleic acid (DNA) coding. X. Meng et al [15] proposed an encryption algorithm based on a one-dimensional e λcos-cot Map for a region of interest in medical DICOM (Digital Imaging and Communications in Medicine). Adaptive thresholding is used to isolate the region of interest from the medical image.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Domain Medical Image Encryption Scheme Using URUK and WAM Chaotic Maps with Wavelet – Fourier Transforms

Abdul-Kareem

Al-Jawher²

2023

JCSANDM

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Image encryption is one of the most important techniques to maintain data confidentiality against illegal access and fraudulent usage. In this study, a new medical image encryption technique was developed by combining the discrete wavelet transform, the fast Fourier transform, the Arnold transform, and two multidimensional chaotic systems. The medical image is subjected to a discrete wavelet transform before the magic square shuffles the image sub-bands. Confusion operations are performed on each scrambled subdomain using the Uruk 4D chaotic system. To increase randomness and unpredictability, a second stage of confusion is implemented in the domain of the Fast Fourier transform using the Arnold transform. The final encrypted image is obtained utilizing secret keys derived from the WAM 3D chaotic system. In particular, the initial conditions for chaotic systems are derived from grayscale values, thereby increasing the algorithm’s sensitivity to the input image. The results of the tests and the security analysis indicate that the proposed algorithm is exceptionally reliable and secure.

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An Encryption Algorithm for Region of Interest in Medical DICOM Based on One-Dimensional eλ-cos-cot Map

Cited by 9 publications

References 55 publications

Partially Deep-Learning Encryption Technique

Partially Deep-Learning Encryption Technique

Explore the potential of deep learning and hyperchaotic map in the meaningful visual image encryption scheme

A Hybrid Domain Medical Image Encryption Scheme Using URUK and WAM Chaotic Maps with Wavelet – Fourier Transforms

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