A unified framework of source camera identification based on features

Source camera identification, which aims at identifying the source camera of an image, has attracted a wide range of attention in the field of digital image forensics recently. Many approaches to source camera identification have been proposed by extracting some image features. However, most of these methods only focused on extracting features from the single artifact of the camera left on the captured images and ignored other artifacts that may help improve final accuracy. Therefore, in this paper, we propose a feature-based framework for source camera identification, which first captures various pure camera-specific artifacts through preprocessing and residual calculation, then extracts discriminative features through image transform, and finally reduces the algorithm complexity through feature reduction. Based on the framework, a novel source camera identification method is proposed, which can identify different camera brands, models and individuals with high accuracy. A large number of comparative experiments show that the proposed method outperforms the state-of-the-art methods.