A pixel-based scrambling scheme for digital medical images protection

Hu, Jiankun; Han, Fengling

doi:10.1016/j.jnca.2009.02.009

Cited by 90 publications

(49 citation statements)

References 22 publications

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“…One sequence is used for row shuffling and another is used for column shuffling. A masking operation [6] is used after row and column shuffling by simple XOR operations between adjacent rows and columns. By values of both sequences, pixel shuffling is done.…”

Section: Image Encryption Techniques 21 Image Encryption Using Linearmentioning

confidence: 99%

Image Encryption using Pseudo Random Number Generators

Banthia¹,

Tiwari²

2013

IJCA

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Image encryption is conversion of image to a distorted form so that it can be secured from unauthorized users. This paper implements and investigates two methods for image encryption. First technique is encryption of image by linear congruential generator. Random numbers are generated by Linear congruential generator. These numbers are used as index for shuffling of rows, columns and pixels of an image. Second technique uses logistic maps to generate random number sequences. These random numbers are used as index for shuffling of rows, columns and pixels of an image. Finally we have analyzed two methods on basis of image quality parameters.

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Section: Image Encryption Techniques 21 Image Encryption Using Linearmentioning

confidence: 99%

Image Encryption using Pseudo Random Number Generators

Banthia¹,

Tiwari²

2013

IJCA

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“…The proposed scheme had classic bi-modular architecture in which the medical image pixels are shuffled via new propose algorithm and also in diffusion process image pixel values are modified by XOR operation. [31][32] Conventional block based image encryption methods are not advised to use for medical image protection due to their large size and increasing demand of telemedicine. A bit level Arnold cat map was adapted for medical image shuffling.…”

Section: Literature Reviewmentioning

confidence: 99%

2D Chaotic Map Based on 2D Adaptive Grey Wolf Algorithm for Ultra Sound Medical Image Security

Koppu¹,

Madhu²

2017

IJIES

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Abstract:In this paper, we have proposed a chaos-based visual encryption method that can be applied to Ultra Sound Medical Images. We used adaptive Grey Wolf Optimization (GWO) to archive Ultra Sound Medical Image encryption. The robustness of the proposed image encryption are measured by various security attacks such as key sensitivity, histogram analysis, adjacent pixel autocorrelation, chi-square test, etc. Moreover, analytical outcomes are compared with the conventional algorithms like Genetic Algorithm (GA) and GWO. The experimental result shows that the proposed method is faster with low complexity.

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“…Hu and Han [15] and Ye [16] proposed a masking method for BMP images on the basis of the chaos map for privacy-protection. However, image compression is necessary for a surveillance camera to store or transmit captured images, and the image compression format used in a surveillance camera is often irreversible, such as with the JPEG compression algorithm.…”

Section: Related Work On Privacy-protection With Security Camerasmentioning

confidence: 99%

Hierarchical-Masked Image Filtering for Privacy-Protection

Kumaki

Fujino

2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents a hierarchical-masked image filtering method for privacy-protection. Cameras are widely used for various applications, e.g., crime surveillance, environment monitoring, and marketing. However, invasion of privacy has become a serious social problem, especially regarding the use of surveillance cameras. Many surveillance cameras point at many people; thus, a large amount of our private information of our daily activities are under surveillance. However, several surveillance cameras currently on the market and related research often have a complicated or institutional masking privacy-protection functionality. To overcome this problem, a Hierarchical-Masked image Filtering (HMF) method is proposed, which has unmaskable (mask reversal) capability and is applicable to current surveillance camera systems for privacy-information protection and can satisfy privacy-protection related requirements. This method has five main features: unmasking of the original image from only the masked image and a cipher key, hierarchical-mask level control using parameters for the length of a pseudorandom number, robustness against malicious attackers, fast processing on an embedded processor, and applicability of mask operation to current surveillance camera systems. Previous studies have difficulty in providing these features. To evaluate HMF on actual equipment, an HMF-based prototype system is developed that mainly consists of a USB web camera, ultra-compact single board computer, and notebook PC. Through experiments, it is confirmed that the proposed method achieves mask level control and is robust against attacks. The increase in processing time of the HMF-based prototype system compared with a conventional non-masking system is only about 1.4%. This paper also reports on the comparison of the proposed method with conventional privacy protection methods and favorable responses of people toward the HMF-based prototype system both domestically and abroad. Therefore, the proposed HMF method can be applied to embedded systems such as those equipped with surveillance cameras for protecting privacy.

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A pixel-based scrambling scheme for digital medical images protection

Cited by 90 publications

References 22 publications

Image Encryption using Pseudo Random Number Generators

Image Encryption using Pseudo Random Number Generators

2D Chaotic Map Based on 2D Adaptive Grey Wolf Algorithm for Ultra Sound Medical Image Security

Hierarchical-Masked Image Filtering for Privacy-Protection

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