Recent Advances in Selective Image Encryption and its Indispensability due to COVID-19

Abstract

The COVID-19 pandemic serves as a grim reminder of the unexpected nature of these outbreaks and gives rise to a unique set of research challenges in a variety of fields. As people all over the world adjust to this new 'normal', with most workplaces, from companies to educational institutions shifting online, enormous surges in the transmission of images and videos have been observed, creating record-breaking stresses on the internet backbone. At the same time, maintaining the privacy and security of the users' data is of immense importance, this is where fast and efficient image encryption algorithms play a vital role. This paper discusses the calamitous effects of the pandemic on the world population and how their changes in multimedia consumption have led to an urgent need for the development and deployment of secure and fast image encryption, especially selective image encryption techniques. It carefully surveys the most recent advances in this field, discusses their real-world effects and finally explores some future research avenues, to provide swift relief and recover from the disastrous effects of the pandemic.

Reversible De-identification of Specific Regions in Biomedical Images and Secured Storage by Randomized Joint Encryption

“…A few survey papers are available on SE with Reversible De-identification (RD) [10][11][12]. Selective Color image encryption with RD is presented in [10].…”

“…In [11], the authors have reviewed different techniques for the SE of multi-media content. In [12], applications of SE in the Covid-19 environment have been presented. In [13], the authors use permutation method for confusion and XOR for diffusion.…”

“…In general, ROI regions are represented by the Region Defining Binary Mask (RD-BM) where the pixels of the ROI are set to 1"s and the other pixels to 0"s. An ROI can be marked manually also by the visual inspection of the image. Once the ROI is determined, the de-identification of that region is carried out by Selective Encryption (SE) [10][11][12][13][14].…”

Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices

“…The current COVID-19 pandemic has cast a disastrous cloud on the health and well-being of people around the globe [6]; in one survey, diabetes was found in about 47% of the hospitalized patients and was associated with severe affliction and mortality [7]. Thus, diabetes and its complication Diabetic Retinopathy pose significant challenges in today's world.…”

A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

“…The outbreak of the novel coronavirus has significantly impacted the livelihood of people across the globe [21] and effective deployment of face mask detection in public places can help in thwarting the transmission of the virus. Face mask detection is a non-trivial problem because of its high throughput, reliability, and privacy requirements; it can't use traditional deployment methods where the image is first sent to a server for classification and the result is sent back for further use in the application.…”