Integration of Spatial and Frequency Domain Encryption for Digital Images
01 Jan 2021-IEEE Access (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 9, pp 149943-149954
TL;DR: In this article, chaos is incorporated for the scrambling of rows and columns of the plaintext image and a noisy image is generated based on chaotic logistic map and the suitable initial conditions which are selected based on the analysis performed.
Abstract: Transmission of multimedia data such as images, videos, and audio over the Internet is risky due to cyberattacks. To overcome the security issues, several encryption schemes are proposed over the last few decades which also possess few vulnerabilities such as time inefficiency and weak security. In this research, to provide the highest level of security to the digital data, chaos is incorporated for the scrambling of rows and columns of the plaintext image. Further, a noisy image is generated based on the chaotic logistic map and the suitable initial conditions which are selected based on the analysis performed. For the reduction of the encryption computational time, a Discrete Wavelet Transform (DWT) is used in which only low-frequency bands are encrypted because most of the plaintext information lies in such frequency bands. To gauge the performance of the proposed encryption scheme, several security tests such as entropy, correlation, energy, peak signal to noise ratio, mean square error, keyspace, and key sensitivity analysis, noise-resistant, and cropping attack analyses are performed. From the cropping and noise attack analysis, we have found that the proposed encryption algorithm can decrypt the plaintext image with negligible loss of information but the content of the plaintext image can be visualized.
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TL;DR: In this article , the authors proposed a novel privacy-preserving non-invasive cancer detection method using deep learning (DL) techniques, which achieved an accuracy of 98.9% and outperformed conventional ML algorithms.
Abstract: Early cancer identification is regarded as a challenging problem in cancer prevention for the healthcare community. In addition, ensuring privacy-preserving healthcare data becomes more difficult with the growing demand for sharing these data. This study proposes a novel privacy-preserving non-invasive cancer detection method using Deep Learning (DL). Initially, the clinical data is collected over the Internet via wireless channels for diagnostic purposes. It is paramount to secure personal clinical data against eavesdropping by unauthorized users that may exploit it for personalized interests. Therefore, the collected data is encrypted before transmission over the channel to prevent data theft. Various security measures, including correlation, entropy, contrast, structural content, and energy, are used to assess the proposed encryption method's efficiency. In this paper, we proposed using the Convolutional Neural Network (CNN)-based model and Magnetic Resonance Imaging (MRI) with different techniques, including transfer learning, fine-tuning, and K-fold analysis cancer detection. Extensive experiments are carried out to evaluate the performance of the proposed DL techniques with regard to traditional machine learning approaches such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results show that the CNN-based model has achieved an accuracy of 98.9% and outperforms conventional ML algorithms. Further experiments demonstrate the efficiency of both encryption schemes, achieving entropy, contrast, and energy of 7.9999, 10.9687, and 0.0151, respectively.
17 citations
TL;DR: Non-invasive sensing-based diagnoses of pneumonia disease are presented, exploiting a deep learning model to make the technique non- invasive coupled with security preservation, and it is found that CNN performs better than other machine learning algorithms.
Abstract: 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.
7 citations
TL;DR: In this paper , a technique for selecting encryption algorithms based on a particular application using pattern recognition and machine learning techniques is presented, and the proposed technique is also compared with the existing techniques to demonstrate its effectiveness.
Abstract: The Internet of Things connects billions of intelligent devices that can interact with one another without human intervention, and during communication, a large amount of data is exchanged between the devices. As a result, it is critical to secure digital data using an encryption technique that provides a suitable degree of security. Numerous existing encryption techniques do not offer sufficient security. Therefore, it is critical to figure out which encryption technique is most appropriate for a particular kind of data. When it comes to manually deciding which encryption technique to use, the process might take a long time. In this research, we present a novel technique for selecting Encryption Algorithms (EAs) based on a particular application using pattern recognition and machine learning techniques. To accomplish this goal, we also prepare a dataset. Several machine learning techniques, such as Support Vector Machines (SVMs), Linear Regression (LR), -Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Trees (DT), and Random Forests (RF), are evaluated. Based on the evaluation, the SVM has been chosen as the best option for the intended technique because its classification accuracy is 98.7%. The experimental results, including accuracy, precision, recall, and F1-score, are used to gauge the performance of the suggested technique. The proposed technique is also compared with the existing techniques to demonstrate its effectiveness.
4 citations
14 Feb 2022
TL;DR: Wang et al. as discussed by the authors proposed a chaotic logistic map-based image encryption technique for multidimensional color images, where instead of encrypting the whole color picture after preprocessing, encrypt each component independently (R, G, and B) to add extra unpredictability to the cipher text image.
Abstract: The security of multimedia data such as audio, video, and images has become a vast research subject due to the fast progress of multimedia technologies. The proposed study aims to develop a novel chaotic logistic map-based image encryption technique for multidimensional color images. Preprocessing is conducted initially in the proposed technique by down sampling the plaintext image. Instead of encrypting the whole color picture after preprocessing, encrypt each component independently (R, G, and B). To add extra unpredictability to the cipher text image, several chaotic mathematical equations are applied. To demonstrate the strength of the suggested work, certain statistical analysis and experimental results are performed, revealing that the new method outperforms the current ones.
3 citations
TL;DR: This paper proposes a new encryption system based on the bit-plane extraction method, chaos theory, and Discrete Wavelet Transform (DWT), and a comparison is made between the statistical security analysis and the existing work to demonstrate that the suggested encryption scheme is better to the existing ones.
Abstract: In the modern era, researchers have focused a great deal of effort on multimedia security and fast processing to address computational processing time difficulties. Due to limited battery capacity and storage, Unmanned Aerial Vehicles (UAVs) must use energy-efficient processing. In order to overcome the vulnerability of time inefficiency and provide an appropriate degree of security for digital images, this paper proposes a new encryption system based on the bit-plane extraction method, chaos theory, and Discrete Wavelet Transform (DWT). Using confusion and diffusion processes, chaos theory is used to modify image pixels. In contrast, bit-plane extraction and DWT are employed to reduce the processing time required for encryption. Multiple cyberattack analysis, including noise and cropping attacks, are performed by adding random noise to the ciphertext image in order to determine the proposed encryption scheme’s resistance to such attacks. In addition, a variety of statistical security analyses, including entropy, contrast, energy, correlation, peak signal-to-noise ratio (PSNR), and mean square error (MSE), are performed to evaluate the security of the proposed encryption system. Moreover, a comparison is made between the statistical security analysis of the proposed encryption scheme and the existing work to demonstrate that the suggested encryption scheme is better to the existing ones.
3 citations
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