Comparative performance study of classification models for image-splicing detection

Abstract

Abstract Recently, images have been manipulated for malicious activities, rather than to enhance their quality. This allows malicious users to make changes to images to create forged versions using digital processing tools. Therefore, the authenticity of digital images has become an important research area because humans cannot observe image forgery processes. The objective of this study was to detect spliced images using three classification techniques—support vector machine (SVM), naive Bayes, and K nearest neighbors (KNN)—to identify the most suitable one. Their classification quality was evaluated using accuracy, sensitivity, and specificity as performance measures. The experimental results showed that the KNN classifier achieved the highest accuracy and sensitivity among the three classifiers. However, naive Bayes achieved the highest specificity among the classifiers.