A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection

The detection of the presence of tampered images is of significant importance in digital forensics. The problem with image tampering detection is the vast number of features that are currently available in the literature. It is very challenging to determine the best features to correctly characterise these images. This paper proposes a hybrid evolutionary framework to perform a quantitative study to evaluate all features in image tampering for the best feature set. Upon feature evaluation and selection, the classification mechanism must be optimised for good performance. Therefore, in addition to being able to determine an optimal set of features for a classifier, the hybrid framework is capable of determining the optimal multiple classifier ensembles while achieving the best classification performance in terms of low complexity and high accuracy for image tampering detection. Using a training dataset of only 5% of the dataset, we were able to obtain accuracies of 90.18% on a CASIA 1 dataset with 1,457 test images, 96.21% on a CASIA 2 dataset with 10,200 and 94.64% on a combined CASIA 1 and 2 dataset with 11,657 testing images. The experiment result shows that our image tampering detection can support large-scale digital image evidence authenticity verification with consistent good accuracy.