The Data Duplicate Download Alert System (DDDAS) is an intelligent and secure framework designed to ensure data integrity, behavioural intelligence, and real-time monitoring in digital environments. The system automatically detects and prevents duplicate or suspicious file downloads by integrating Flask-based backend automation, AI- driven analytics, and blockchain-enabled immutability. Every file uploaded or downloaded within DDDAS is recorded as a block in the blockchain ledger, where each block contains the file’s unique hash value, timestamp, and user details, forming a tamper-proof digital record. This blockchain-based mechanism guarantees immutability—once a file’s data is stored, it cannot be altered or deleted—thus ensuring complete data integrity and enabling the system to identify any unauthorized or repeated file activity with high accuracy. The backend, developed using Flask, manages secure hash generation and blockchain synchronization, while the frontend, implemented using Next.js and Tailwind CSS, provides an interactive dashboard for real-time visualization of transactions and AI- generated alerts. The system’s integration of Google Gemini AI allows contextual analysis of user behaviour to detect anomalies and assess potential risks beyond conventional rule- based systems. By combining blockchain transparency, AI intelligence, and Flask-based monitoring, DDDAS enhances trust, traceability, and accountability across all file operations. It serves as a next-generation cybersecurity solution that not only protects against redundant downloads and data manipulation but also establishes a transparent, verifiable, and tamper-proof environment for digital asset management.

The Data Duplicate Download Alert System successfully addresses one of the most common yet often overlooked issues in data management the unnecessary duplication of downloaded files. By implementing a smart, hash-based detection mechanism, the system ensures that users are immediately notified if they attempt to download a file that already exists in their repository. This not only prevents redundant storage usage but also enhances overall system efficiency and user productivity. The project’s Flask-based architecture, combined with a lightweight metadata.json storage structure, delivers a highly responsive and reliable platform for real-time duplicate detection. The integration of cryptographic hashing algorithms such as MD5 and SHA-256 ensures high accuracy in identifying identical files, even when filenames differ. The alert notification mechanism improves user awareness, while the intuitive web interface provides a seamless experience for initiating downloads, viewing history, and managing alerts. Through extensive testing and validation, the system demonstrated excellent performance in detecting duplicates across multiple file formats and sizes, with near- instantaneous response times. The modular design allows each component from the duplicate checker to the alert system to function independently and cohesively. This ensures that the system can be easily maintained, upgraded, and scaled according to future requirements.

[1]T. Nguyen and J. Li, “AI-Based Data Verification Techniques,” International Journal of Artificial Intelligence Research, vol. 12, no. 4, pp. 145–153, 2020. [2]R. Patel, “Data Extraction for Automation,” Journal of Intelligent Systems and Applications, vol. 9, no. 2, pp. 98–107, 2021. [3]S. Gupta, “Automated Data Consistency Analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 1201–1210, 2021. [4]L. Wang, “Deep Learning for Data Extraction,” Journal of Computer Vision and Machine Intelligence, vol. 14, no. 5, pp. 243–252, 2022. [5]H. Kim, “Hybrid Document Processing Model,” International Conference on Computational Intelligence and Applications, pp. 67–74, 2022. [6]P. Sharma, “Web Data Parsing Framework,” International Journal of Web Engineering, vol. 11, no. 3, pp. 190–198, 2023. [7]M. Shah, “Metadata-Driven Extraction,” ACM Journal of Data Science and Engineering, vol. 7, no. 2, pp. 105–112, 2023. [8]A. Kumar, “AI Validation for Documents,” IEEE Access, vol. 12, no. 1, pp. 450– 458, 2024. [9]Z. Ali, “Automated Form Reader,” International Journal of Image and Data Processing, vol. 15, no. 2, pp. 210–218, 2024. [10]K. Roy, “Smart Data Integrity Checker,” Proceedings of the International Conference on Data Analytics and AI Systems, pp. 90–98, 2025.