Image Encryption Using Dynamic S-Box Substitution in the Wavelet Domain

Shafique, Arslan; Ahmed, Fawad

doi:10.1007/s11277-020-07680-w

Cited by 58 publications

(25 citation statements)

References 48 publications

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“…In [150], Shafique et al addressed the issues of pixel hiding of the original image with low textured regions and increased computational time which are often present in traditional image encryption techniques. To overcome these issues the author has proposed dynamic substitution S-box encryption in a discrete wavelet transform (DWT).…”

Section: Image Encryption In Transform Domainmentioning

confidence: 99%

Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains

Zia

McCartney

Scotney

et al. 2022

Int. J. Inf. Secur.

102

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Chaos-based cryptosystems have been an active area of research in recent years. Although these algorithms are not standardized like AES, DES, RSA, etc., chaos-based cryptosystems like Chebyshev polynomials can provide additional security when used with standard public key cryptosystems like RSA and El-gamal. Standard encryption algorithms such as AES have always been the primary choice, but when it comes to image or video encryption, many researchers recommend chaos-based encryption techniques due to their computational efficiency. This paper presents a survey on the most up-to-date chaos-based image encryption techniques and classifies them into spatial, temporal and spatiotemporal domains for better understanding. The significant improvements in the field of image encryption are discussed. In addition, comparative analysis is performed to validate the evaluation matrices for quantifying the encryption algorithms’ security and performance in recent papers.

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Section: Image Encryption In Transform Domainmentioning

confidence: 99%

Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains

Zia

McCartney

Scotney

et al. 2022

Int. J. Inf. Secur.

102

View full text Add to dashboard Cite

show abstract

“…To gauge the performance of the proposed encryption algorithm, the following parameters are under consideration: The detail of such security parameters can be seen in [65][66][67]. Table 1 shows the statistical value of the security parameters calculated for the evaluation of the proposed cryptosystem for medical images.…”

Section: Proposed Encryption Scheme Evaluationmentioning

confidence: 99%

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Rehman

Shafique

Khan

et al. 2022

Sensors

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This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

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“…From the last few decades, several chaos-based image encryption schemes have been suggested; however, numerous of them have been proven unsecured because of lower keyspace and computational difficulties [45]. In accordance with the encryption schemes presented in the literature [29], [46]- [48], a secure encryption scheme must hold both diffusion and permutation mechanisms. But unfortunately, several existing schemes do not fulfill the above mentioned [49], [50].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps

et al. 2021

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The advancement in wireless communication has encouraged the process of data transferring through the Internet. The process of data sharing via the Internet is prone to several attacks. The sensitive information can be protected from hackers with the help of a process called Encryption. Owing to the increase in cyberattacks, encryption has become a vital component of modern-day communication. In this article, an image encryption algorithm is suggested using dynamic substitution and chaotic systems. The suggested scheme is based upon the chaotic logistic map, chaotic sine maps and the dynamical substitution boxes (S-boxes). In the proposed scheme, the S-box selection is according to the generated sequence by deploying the chaotic sine map. To evaluate the robustness and security of the proposed encryption scheme, different security analysis like correlation analysis, information entropy, energy, histogram investigation, and mean square error are performed. The keyspace and entropy values of the enciphered images generated through the proposed encryption scheme are over 2 278 and 7.99 respectively. Moreover, the correlation values are closer to zero after comparison with the other existing schemes. The unified average change intensity (UACI) and the number of pixel change rate (NPCR) for the suggested scheme are greater than 33, 99.50% respectively. The simulation outcomes and the balancing with state-of-the-art algorithms justify the security and efficiency of the suggested scheme.

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Image Encryption Using Dynamic S-Box Substitution in the Wavelet Domain

Cited by 58 publications

References 48 publications

Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains

Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps

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