Pooja, S and Raju, Laiju K and Chhapekar, Utkarsh and Chandrakala, C B (2021) Face Detection using Deep Learning to ensure a Coercion Resistant Blockchain-based Electronic Voting. Engineered Science, 16. pp. 341-353. ISSN 2576-988X
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Abstract
An election is a powerful tool of any democratic country, which allows every citizen to exercise their right to vote. Though in�person voting is the most widely used medium for a citizen to vote, circumstances like a pandemic, natural disasters, or other location-based issues could deter an individual from doing so. This work proposes an architecture for a mobile-based internet voting application that could be downloaded on a smartphone, allowing citizens to vote remotely. Existing web-based online voting systems are complicated for novice users, requiring a complex key management process and a lack of coercion resistance voting system. The proposed work is a mobile application, which ensures more population coverage. The work also suggests a key management system for regional election officers, thus freeing novice users from the complications of key management. A face detector is proposed to provide coercion resistance in this online voting system. Face detection uses a deep learning-based multi-task cascaded convolutional neural network (MTCNN). The proposed model has also incorporated multi-factor authentication, blockchain technology, and asymmetric encryption standards to ensure security features required in a voting system while providing a hassle-free voting experience to the voter
Item Type: | Article |
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Uncontrolled Keywords: | Blockchain; CNN; Face Detection; Paillier Threshold System; Remote electronic voting |
Subjects: | Engineering > MIT Manipal > Information and Communication Technology |
Depositing User: | MIT Library |
Date Deposited: | 03 Mar 2022 06:00 |
Last Modified: | 03 Mar 2022 06:00 |
URI: | http://eprints.manipal.edu/id/eprint/158273 |
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