Enhancing Aadhar Card Image Security with Machine Learning-Based Face Morphing Detection

In this study on digital image forensics, the focus is on detecting face morphing in Aadhar card images for reliable identity verification. The proposed approach integrates advanced image processing techniques, including Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Local Phase Quantization (LPQ), to extract detailed features from both authentic and morphed Aadhar card images. Machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF), and KNN (k-nearest neighbors) are employed to assess the effectiveness of the proposed methods. The research highlights the success of combining LBP, HOG, and LPQ in detecting face morphing and offering significant insights into the appropriateness of various classifiers in the realm of image forensics, as measured by metrics like accuracy, precision, recall, and f1-score.