Encryption of medical images based on the cosine number transform

Lima, Juliano B.; Madeiro, Francisco; Sales, Fernando José Ribeiro

doi:10.1016/j.image.2015.03.005

Cited by 97 publications

(46 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The cosine transform over a finite field is known as the finite field cosine transform (FFCT), which is defined in [16]. This FFCT has been used in image encryption [17][18][19] with a high level security over the encrypted image. The decrypted images obtained for the decryption system based on the use of FFCT have a high level of image quality in comparison to the image quality of the resulting decrypted images in several optical decryption systems, because the FFCT over a finite field have no rounding and overflow problems [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Image Encryption and Decryption Systems Using the Jigsaw Transform and the Iterative Finite Field Cosine Transform

2019

Photonics

View full text Add to dashboard Cite

We propose the use of the Jigsaw transform (JT) and the iterative cosine transform over a finite field in order to encrypt and decrypt images. The JT is a nonlinear operation that allows one to increase the security over the encrypted images by adding new keys to the encryption and decryption systems. The finite field is a finite set of integer numbers where the basic mathematical operations are performed using modular arithmetic. The finite field used in the encryption and decryption systems has an order given by the Fermat prime number 257. The iterative finite field cosine transform (FFCT) was used in our work with the purpose of obtaining images that had an uniform random distribution. We used a security key given by an image randomly generated and uniformly distributed. The JT and iterative FFCT was utilized twice in the encryption and decryption systems. The encrypted images presented a uniformly distributed histogram and the decrypted images were the same original images used as inputs in the encryption system. The resulting decrypted images had a high level of image quality in comparison to the image quality of the decrypted images obtained by the actual optical decryption systems. The proposed encryption and decryption systems have three security keys represented by two random permutations used in the JTs and one random image. The key space of the proposed encryption and decryption systems is larger. The previous features of the security system allow a better protection of the encrypted image against brute force and statistical analysis attacks.

show abstract

Section: Introductionmentioning

confidence: 99%

Image Encryption and Decryption Systems Using the Jigsaw Transform and the Iterative Finite Field Cosine Transform

2019

Photonics

View full text Add to dashboard Cite

show abstract

“…The normalized entropy [6] of the ciphered image is defined aswhere P is the number of different values that the pixels of the ciphered image can assume, is the amount of pixels of the ciphered image that assume value i , and N is the total amount of pixels of the ciphered image. During the experiments, we found that the ciphered pixels’ intensity of all the algorithms mentioned in the paper are almost equiprobable, so their normalized entropy are all approximate to 1.…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

“…In recent years, numerous studies on encryption of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), have been reported [1–6], although most of them did not consider compression during encryption. The storage, transmission, and retrieval of massive bio-information should meet several compulsory requirements [7]: (1) high efficiency for rapid transmission and prompt retrieval; (2) strict information security to guarantee users’ privacy; and (3) high data fidelity to preserve the pathological information.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz

et al. 2016

View full text Add to dashboard Cite

BackgroundThe existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors.MethodsWe propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz.ResultsThe proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder.ConclusionsNumerical simulation results verify the validity and the reliability of this scheme.

show abstract

“…Nonetheless, a number of approaches, based on conventional encryption schemes such as AES, has been proposed to protect medical records in the DICOM system (31) and in many other research papers (32)- (34). However, traditional cryptographic algorithms are defined mainly to protect textual data.…”

Section: Problem Definitionmentioning

confidence: 99%

A dynamic approach for a lightweight and secure cipher for medical images

Noura

Chehab

et al. 2018

Multimed Tools Appl

View full text Add to dashboard Cite

Protecting the contents of medical records is of paramount importance when it comes to preserving patients' privacy. Most existing cryptographic-based solutions rely on traditional encryption algorithms having a multi-round structure, which introduces processing latency and requires increased resources. Medical images possess special characteristics compared to other types of images. The main goal of this paper is to leverage these characteristics to design and implement an efficient and secure encryption algorithm for such images. The proposed solution defines three variants of encryption algorithms: (a) full, (b) middlefull, and (c) selective. The full approach encrypts all sub-matrices of an image, while the middle-full variant is a middle solution between the selective and full algorithms and its goal is to just hide the type of the medical image. Selective encryption identifies a set of sub-matrices of an image according to a statistical average test, known as region of interest (ROI). In the three approaches, a high security level is ensured since each image is encrypted independently of the previous and next images. Also, all primitives of the proposed cipher, such as permutation and substitution, depend on a dynamic key. Furthermore, the encryption scheme is efficient since the proposed round function is lightweight and applied for only one round. This reduces the latency and the required resources as compared to traditional cryptographic schemes. The proposed approach is flexible as it can be applied in either selective, middle-full, or full mode. Also, the size of a sub-matrix is variable and can be changed according to the available memory size. Several security and performance tests are conducted to evaluate the effectiveness of the proposed solution. The results validate the robustness of the proposed scheme against almost all considered types of attacks and show an improvement in terms of latency and resources compared to current image-encryption schemes. Also, the results confirm the robustness of the proposed algorithm in protecting the contents of medical images.

show abstract

Encryption of medical images based on the cosine number transform

Cited by 97 publications

References 27 publications

Image Encryption and Decryption Systems Using the Jigsaw Transform and the Iterative Finite Field Cosine Transform

Image Encryption and Decryption Systems Using the Jigsaw Transform and the Iterative Finite Field Cosine Transform

Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz

A dynamic approach for a lightweight and secure cipher for medical images

Contact Info

Product

Resources

About