The rapid development of deep learning techniques has created new challenges in identifying the origin of digital images because generative adversarial networks and variational autoencoders can create plausible digital images whose contents are not present in natural scenes. In this paper, we consider the origin that can be broken down into three categories: natural photographic image (NPI), computer generated graphic (CGG), and deep network generated image (DGI). A method is presented for effectively identifying the origin of digital images that is based on a convolutional neural network (CNN) and uses a local-to-global framework to reduce training complexity. By feeding labeled data, the CNN is trained to predict the origin of local patches cropped from an image. The origin of the full-size image is then determined by majority voting. Unlike previous forensic methods, the CNN takes the raw pixels as input without the aid of residual map. Experimental results revealed that not only the high-frequency components but also the middle-frequency ones contribute to origin identification. The proposed method achieved up to 95.21% identification accuracy and behaved robustly against several common post-processing operations including JPEG compression, scaling, geometric transformation, and contrast stretching. The quantitative results demonstrate that the proposed method is more effective than handcrafted feature-based methods.
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