Quantum Dot-Based Hidden Markers with Machine Learning Algorithms for Enhanced Forensic Investigation and Document Security

This study investigates the integration of advanced machine learning algorithms with quantum dot (QD)-based hidden markers to enhance the security and authenticity of forensic investigations and document protection. Quantum dots, known for their unique optical properties, offer a robust mechanism for embedding invisible markers that are resistant to forgery and tampering. By coupling these markers with machine learning algorithms, rapid detection, classification, and verification of hidden patterns in potentially compromised or suspicious documents can be achieved. The proposed model was tested on various substrates and demonstrated high durability and precision, achieving a detection accuracy of 92%. This research also explores the versatility of QD markers in a range of forensic applications, demonstrating their adaptability to different environmental conditions. The model's ability to generalize across varied datasets accentuates its practical utility in realworld forensic scenarios. These findings highlight the potential of this approach in automating forensic analysis and improving document security by providing an advanced framework for document authenticity verification.