Published Paper Details:

The integration of fake biometric detection using blockchain and thermal imaging technology presents a pioneering approach to bolstering the security and transparency of voting systems. By leveraging Convolutional Neural Networks (CNNs) in thermal image recognition, alongside the immutable ledger of blockchain, this innovative solution ensures the integrity of biometric data. Multi-CNNs use multiple channels as input, allowing the network to process different types of data or features simultaneously. Each thermal image, along with associated data, is securely stored on the blockchain, employing cryptographic hashes to link consecutive blocks, thereby preventing tampering or manipulation. The voting process becomes inherently transparent and traceable, fostering trust among stakeholders. Furthermore, smart contracts play a pivotal role in automating processes and enforcing predefined rules within the voting system. These self-executing contracts, encoded with the terms of the agreement, can trigger alerts or execute predetermined actions if anomalies are detected in the thermal image data, such as temperature breaches beyond acceptable limits. This proactive approach enhances the security posture of the voting system, mitigating potential risks and ensuring the authenticity of each vote cast. By combining fake biometric detection, thermal imaging technology, and blockchain-enabled transparency, this novel solution sets a new standard for secure and trustworthy electoral processes, safeguarding the democratic foundation of societies. SHA-256 is considered to be secure against collision attacks, where two different inputs produce the same hash output, and pre-image attacks, where the original input is derived from the hash output, when used correctly.