Hybrid Feature Selection for Effective Copy-Move Forgery Detection

In today's digital landscape, the threat of digital image forgery is on the rise, necessitating the development of advanced detection and authentication techniques.Copy-move image forgery, a prevalent form of manipulation, involves duplicating a portion of an image and inserting it elsewhere with malicious intent.Detecting such forgeries is of utmost importance.This research introduces an innovative approach that combines convolutional neural network (CNN)-based deep features and gray-level co-occurrence matrix (GLCM) features.These hybrid features are carefully selected using neighborhood component analysis (NCA) to optimize their discriminative power.Subsequently, a support vector machine (SVM) classifier is employed to classify these refined hybrid features, resulting in exceptional detection performance.The proposed method achieves remarkable accuracy in forgery detection.In the CASIA 1 dataset, the hybrid features-NCA-SVM method outperforms other techniques with an accuracy of 97.62%.Similarly, on the MICC-F220 dataset, the hybrid features-NCA-SVM approach attains the highest accuracy of 98.00%.These results underscore the robustness and versatility of the proposed method in detecting copy-move forgeries across different datasets.