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

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.