Computational Image Encryption Techniques: A Comprehensive Review

Kaur, Mandeep; Singh, Sarva Jit; Kaur, Manjit

doi:10.1155/2021/5012496

Cited by 33 publications

(21 citation statements)

References 156 publications

(149 reference statements)

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“…Five well-known quality metrics, i.e., root mean square error (RMSE) [ 46 ], universal image quality index (UIQI) [ 47 ], correlation coefficient (CC) [ 46 ], spectral angle mapper (SAM) [ 46 ], and Erreur relative globale adimensionnelle de synthese (ERGAS) [ 48 ], are used for comparative analysis.…”

Section: Performance Analysismentioning

confidence: 99%

[Retracted] Artificial Intelligence‐Based Deep Fusion Model for Pan‐Sharpening of Remote Sensing Images

Iskanderani

Mehedi

Aljohani

et al. 2021

Computational Intelligence and Neuroscience

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During the past two decades, many remote sensing image fusion techniques have been designed to improve the spatial resolution of the low-spatial-resolution multispectral bands. The main objective is fuse the low-resolution multispectral (MS) image and the high-spatial-resolution panchromatic (PAN) image to obtain a fused image having high spatial and spectral information. Recently, many artificial intelligence-based deep learning models have been designed to fuse the remote sensing images. But these models do not consider the inherent image distribution difference between MS and PAN images. Therefore, the obtained fused images may suffer from gradient and color distortion problems. To overcome these problems, in this paper, an efficient artificial intelligence-based deep transfer learning model is proposed. Inception-ResNet-v2 model is improved by using a color-aware perceptual loss (CPL). The obtained fused images are further improved by using gradient channel prior as a postprocessing step. Gradient channel prior is used to preserve the color and gradient information. Extensive experiments are carried out by considering the benchmark datasets. Performance analysis shows that the proposed model can efficiently preserve color and gradient information in the fused remote sensing images than the existing models.

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Section: Performance Analysismentioning

confidence: 99%

[Retracted] Artificial Intelligence‐Based Deep Fusion Model for Pan‐Sharpening of Remote Sensing Images

Iskanderani

Mehedi

Aljohani

et al. 2021

Computational Intelligence and Neuroscience

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“…There are 256 options available for both a and b for the affine mapping ψ i : R −→ R. So the key space is approximately ((10 15 ) 6 × (10 15 ) 5 × 256 × 256 ≈ 2 564 . A robust encryption process demands a key space of at least 2 100 [44], which means that our technique is undefeatable and cannot be invaded by brute force attack.…”

Section: Key Space Analysismentioning

confidence: 99%

An RGB Image Encryption Scheme based on Amalgamation of 3D-Dynamical Systems and a Left Almost ring

Haider,

Sanam

2023

Preprint

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Digital images are widely used in the areas of medicine, defense, and space technology but their safeguarding and secure transmission is becoming a prevalent issue for researchers and engineers. Image encryption techniques usually make use of commutative and associative algebraic structures like finite Galois fields and unitary commutative rings. In this paper, first, a non-associative LA-ring of order 256 is obtained using computational techniques, and then this LA-ring and the three-dimensional non-linear chaotic Rucklidge system are combined to generate three chaotic-algebraic sequences which are used to cause confusion in encryption. For the sake of diffusion, the three dimensional non-linear chaotic Genesio-Tesi system is utilized. The motivation behind using an LA ring is to boast the robustness and increase the key space due to this algebraic structure's non-associative and non-commutative properties. Furthermore, the number of control parameters has increased because of two, three dimensional chaotic systems which aid in expanding the key space and increase encryption efficiency thus providing better security. Performance metrics are carried out for encrypted images and a comparison with some of the existing chaos dependent schemes is made, proving that the suggested technique approaches the standard prime level and is good enough for encryption processes and real-time communication.

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“…On the basis of several ideas, including optical, compressive sensing, and chaotic maps image encryption, the existing image encryption methods are divided into numerous categories. The methods currently used to access the various security settings are compared [16]. A hyperchaotic system-based image encryption technique and a variable-step Josephus problem were also mentioned in the study [17].…”

Section: Literature Review and Problem Statementsmentioning

confidence: 99%

Evaluating image encryption algorithms for the hyperchaotic system and fibonacci q-matrix, secure internet of things, and advanced encryption standard

Hussein

Hamza

Al-Shoukry

et al. 2022

EEJET

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In the era of information technology, users had to send millions of images back and forth daily. It's crucial to secure these photos. It is important to secure image content using digital image encryption. Using secret keys, digital images are transformed into noisy images in image encryption techniques, and the same keys are needed to restore the images to their original form. The majority of image encryption methods rely on two processes: confusion and diffusion. However, previous studies didn’t compare recent techniques in the image encryption field.This research presents an evaluation of three types of image encryption algorithms includinga Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and AES techniques. The Fibonacci Q-matrix in the hyperchaotic technique makes use of a six-dimension hyperchaotic system's randomly generated numbers and confuses the original image to dilute the permuted image. The objectives here areto analyze the image encryption process for the Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and Advanced Encryption Standard (AES), and compare their encryption robustness. The discussed image encryption techniques were examined through histograms, entropy, Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and correlation coefficients. Since the values of the Chi-squared test were less than (293) for the Hyperchaotic System & Fibonacci Q-matrix method, this indicates that this technique has a uniform distribution and is more efficient. The obtained results provide important confirmation that the image encryption using Fibonacci Q-matrix in hyperchaotic algorithm performed better than both the AES and SIT based on the image values of UACI and NPCR.

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Computational Image Encryption Techniques: A Comprehensive Review

Cited by 33 publications

References 156 publications

[Retracted] Artificial Intelligence‐Based Deep Fusion Model for Pan‐Sharpening of Remote Sensing Images

[Retracted] Artificial Intelligence‐Based Deep Fusion Model for Pan‐Sharpening of Remote Sensing Images

An RGB Image Encryption Scheme based on Amalgamation of 3D-Dynamical Systems and a Left Almost ring

Evaluating image encryption algorithms for the hyperchaotic system and fibonacci q-matrix, secure internet of things, and advanced encryption standard

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