Securing DICOM images based on adaptive pixel thresholding approach

This paper presents a novel efficient two-region Selective encryption approach that exploits medical images statistical properties to adaptively segment Digital Imaging and Communications in Medicine (DICOM) images into regions using thresholding technique in the spatial domain. This approach uses adaptive pixel thresholding, in which thresholds for same DICOM modality, anatomy part and pixel intensities' range were extracted off-line. Then, the extracted thresholds were objectively and subjectively evaluated to select the most accurate threshold for the correspondent pixel intensities' range. In the on-line stage, DICOM images were segmented into a Region Of Interest (ROI) and a Region Of Background (ROB) based on their pixels intensities using the adopted thresholds. After that, ROI was encrypted using Advanced Encryption Standard (AES), while ROB was encrypted using XXTEA. The main goal of the proposed approach is to reduce the encryption processing time overhead in comparison with the Naïve approach; where all image pixels are encrypted using AES. The proposed approach aims to achieve a trade-off between processing time and a high level of security. The encryption time of the proposed approach can save up to 60% of the Naïve encryption time for DICOM images with small-medium ROI

A Study of Data Security on E-Governance using Steganographic Optimization Algorithms

Steganography has been used massively in numerous fields to maintain the privacy and integrity of messages transferred via the internet. The need to secure the information has augmented with the increase in e-governance usage. The wide adoption of e-governance services also opens the doors to cybercriminals for fraudulent activities in cyberspace. To deal with these cybercrimes we need optimized and advanced steganographic techniques. Various advanced optimization techniques can be applied to steganography to obtain better results for the security of information. Various optimization techniques like particle swarm optimization and genetic algorithms with cryptography can be used to protect information for e-governance services. In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented. A new perspective on using this technique to protect the information for e-governance is also presented. Deep Learning might be the area that can be used to automate the steganography process in combination with other method

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes

Balancing Compression and Encryption of Satellite Imagery

With the rapid developments in the remote sensing technologies and services, there is a necessity for combined compression and encryption of satellite imagery. The onboard satellite compression is used to minimize storage and communication bandwidth requirements of high data rate satellite applications. While encryption is employed to secure these resources and prevent illegal use of image sensitive information. In this paper, we propose an approach to address these challenges which raised in the highly dynamic satellite based networked environment. This approach combined compression algorithms (Huffman and SPIHT) and encryptions algorithms (RC4, blowfish and AES) into three complementary modes: (1) secure lossless compression, (2) secure lossy compression and (3) secure hybrid compression. The extensive experiments on the 126 satellite images dataset showed that our approach outperforms traditional and state of art approaches by saving approximately (53%) of computational resources. In addition, the interesting feature of this approach is these three options that mimic reality by imposing every time a different approach to deal with the problem of limited computing and communication resources

Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography

Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods

An Adaptive Image Encryption Scheme Guided by Fuzzy Models

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.Comment: Iranian Journal of Fuzzy Systems (2023