Advanced Watermarking Techniques for Enhanced Biometric Security Utilizing Machine Learning Models

Abstract

Abstract - The project presents an innovative biometric watermarking system that utilizes the Rubik algorithm to encrypt iris and fingerprint images, generating a distinct and secure watermark. By integrating Convolutional Neural Networks (CNN), the system proficiently distinguishes between genuine biometric features and forgeries, thereby enhancing the security of document authentication. The incorporation of adaptive learning allows for continuous improvements in detection capabilities while providing robust protection against fraudulent access attempts. This advanced solution merges cutting-edge encryption techniques with machine learning to safeguard the integrity of biometric data, strengthen authentication processes, and effectively address potential security threats. It marks a significant advancement in the protection of sensitive information. Keywords - Biometric watermarking, Machine learning, Rubik algorithm, Adaptive learning, Convolutional Neural Networks (CNN), Document authentication