Ensemble classifier based source camera identification using fusion features

Source camera identification, which means identifying the camera source of a given image, has become one of the most important branches of digital image forensics. In order to improve the detection accuracy, the feature dimensions used in existing methods are increasing, and consequently Support Vector Machine (SVM) seems no longer applicable. In this paper, an ensemble classifier is introduced into to source camera identification, which uses the fusion features to capture software-related, hardware-related, and statistical characteristics left on the images by digital camera. Experimental results indicate that the proposed method can achieve near 100% accuracy for camera brand and model identification, and also outperforms the baseline methods in identifying different camera individuals.