AI bot to detect fake COVID‐19 vaccine certificate
IET information security
; 2022.
Article
in English
| EuropePMC | ID: covidwho-1980214
ABSTRACT
As the world is now fighting against rampant virus COVID‐19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non‐COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1‐score, and computation time over state‐of‐art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201‐LBP) outperforms with an accuracy of 0.94.
Search on Google
Collection:
Databases of international organizations
Database:
EuropePMC
Topics:
Vaccines
Language:
English
Journal:
IET information security
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS