Design and Implementation of Image Recognition Algorithm for Financial Document Tampering Detection

With the flourishing development of digital finance, electronic financial documents have become the mainstream medium, while simultaneously presenting escalating challenges of document tampering risks. Traditional manual review methods can no longer meet the real-time detection demands of massive documents, creating an urgent need for efficient and reliable automated detection solutions. This research addresses the challenge of financial document tampering detection by designing an intelligent detection system based on deep learning. The system enhances feature description capabilities through an improved SIFT algorithm, effectively extracting local feature information from document images. It employs an optimized U-Net network architecture to achieve precise localization of tampered regions and introduces ensemble learning strategies to significantly improve the accuracy of tampering type recognition. In practical applications, the system demonstrates exceptional performance with a detection accuracy of 98.6%, an average processing time of just 300 ms, and the capability to process over 4.15 million documents monthly. Large-scale experimental validation confirms that the system possesses high detection precision, fast processing speed, and strong scalability, effectively resolving the efficiency bottleneck faced by financial institutions in document security review. The research outcomes not only provide financial institutions with a reliable document security assurance solution but also establish a new paradigm for innovative applications of deep learning technology in financial security.