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January 1, 2025International Journal of Applied Cryptography

Secure voter authentication using (2, 2) visual cryptography and convolutional neural network

Sneha Annappanavar Pallavi Vijay Chavan

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Key Points

The research aims to develop a secure method for voter authentication using visual cryptography and convolutional neural networks.
Utilized a (2, 2) visual cryptography scheme for secure data sharing.
Implemented a convolutional neural network to validate voter identities.
Conducted theoretical analysis and experimental validation of the authentication process.
Achieved a high accuracy rate in voter authentication.
Enhanced security compared to traditional methods of verification.
Demonstrated effectiveness of the (2, 2) visual cryptography approach in protecting sensitive voter information.

Implication

Research demonstrates secure voter authentication using visual cryptography and convolutional neural networks, suggesting enhanced election security.

Abstract

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Cite This Study

Annappanavar et al. (2025) studied this question.

synapsesocial.com/papers/69abc0de5af8044f7a4e987a https://doi.org/10.1504/ijact.2025.10076756

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