An Image Feature Extraction to Generate a Key for Encryption in Cyber Security Medical Environments

Abstract

Cyber security is a term utilized for describing a collection of technologies, procedures, and practices that try protecting an online environment of a user or an organization. For medical images among most important and delicate data kinds in computer systems, the medical reasons require that all patient data, including images, be encrypted before being transferred over computer networks by healthcare companies. This paper presents a new direction of the encryption method research by encrypting the image based on the domain of the feature extracted to generate a key for the encryption process. The encryption process is started by applying edges detection. After dividing the bits of the edge image into (3×3) windows, the diffusions on bits are applied to create a key used for encrypting the edge image. Four randomness tests are passed through NIST randomness tests to ensure whether the generated key is accepted as true. This process is reversible in the state of decryption to retrieve the original image. The encryption image that will be gained can be used in any cyber security field such as healthcare organization. The comparative experiments prove that the proposed algorithm improves the encryption efficiency has a good security performance, and the encryption algorithm has a higher information entropy 7.42 as well as a lower correlation coefficient 0.653.

Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence

“…This has been conducted by the luminosity method. The latter indicates that the contribution of the Red color must be reduced, the contribution of the Green color must be increased, and the contribution of the Blue color must be placed between these two [26,27]. Samples of the generated grayscale images are portrayed in Figure 3.…”

“…This method can replace thousands of features with hundreds or less. Such features are referred to as weak classifiers [26,31].…”

A Partial Face Encryption in Real World Experiences Based on Features Extraction from Edge Detection

“…Corners can be determined according to on the USAN zone. When the zone of USAN is up to the tiny, like status "a," the nucleus is within the corner [44,[46][47][48][49]. (…”

“…g refers to geometric threshold that locates the sharp scale of corner. ultimately, corners being establish via non-maximum inhibition [46,[48][49][50]. Suggested method A partial facial encryption manner in real world experiences is offered in the suggested manner function.…”