VeriFIR
Unbreakable Records, Intelligent Justice
The problem VeriFIR solves
VeriFIR tackles critical issues in traditional FIR (First Information Report) systems, with a primary focus on false cases, record tampering, and human error, which undermine legal integrity and public trust. False cases, often orchestrated by corrupt officials or individuals with malicious intent, are a pervasive problem. VeriFIR counters this using the Ethereum blockchain’s immutability, ensuring that once an FIR is recorded, it cannot be altered. The system’s timestamp feature provides evidence to challenge subsequent falsifications, empowering the accused. Record tampering, facilitated by centralized control and inadequate security, is another major concern. VeriFIR’s decentralized architecture distributes data across the network, making unauthorized changes nearly impossible. Real-time access for courts and cross-jurisdictional police stations further deters and detects tampering, enhancing transparency. Human error, such as incorrect legal sections under the Bharatiya Nyaya Sanhita (BNS) or mistakes in manual data entry, also plagues traditional systems, leading to injustices. VeriFIR mitigates this with a BNS-trained chatbot, enabling users to self-verify applicable sections, and an OCR model that automates data extraction from physical FIRs, reducing manual errors. Together, these solutions foster a reliable, tamper-proof, and accurate FIR process.
Challenges we ran into
Developing VeriFIR, a machine learning and blockchain-based FIR management system, presented several significant challenges that required careful resolution. One of the primary hurdles was reducing the gas price of the smart contract, a critical concern due to the extensive data fields—over 24 parameters across multiple sub-structs (FIRCore, IncidentInfo, ComplainantInfo, and CaseDetails)—which increased computational complexity and storage demands on the Ethereum blockchain. This high gas cost stemmed from the large number of string variables and the multi-step filing process (fileFIR, setIncidentInfo, setComplainantInfo, and setCaseDetails), each requiring separate transactions. Optimizing the contract involved restructuring data storage, leveraging efficient encoding techniques, and splitting functionality to minimize gas consumption, ensuring affordability for deployment. Another major issue was deploying the contract on the Ethereum blockchain, complicated by network congestion and the need for a robust testnet like Sepolia, alongside securing sufficient test ETH and configuring Infura for reliable connectivity. Additionally, compiling everything posed a significant challenge, as integrating the Solidity smart contract with Hardhat, the React frontend, and the machine learning components (OCR and chatbot) demanded meticulous dependency management, version compatibility (e.g., Node.js, Solidity), and artifact generation, often leading to errors that required iterative debugging to achieve a cohesive, functional system.
Tracks Applied (2)