Abstract
Computer generated (CG) images have been gradually overspread on the Internet, resulting in difficult discrimination from natural images (NIs) captured by an authentic imaging device. Although some discriminators can deal with NIs in JPEG format, the classification between uncompressed NIs (that are possibly generated in any imaging procedure before compression) and CG ones still remains unknown. Thus, this paper aims to establish multiple discriminators classifying between NIs and CG images. We first describe the main imaging procedure and its intrinsic property, which characterizes the discriminative features for classification. Then, the residual noise (representing intrinsic characteristic) is extracted. Its statistical distribution indeed helps us establish multiple discriminators, consisting of the generalized likelihood ratio test (GLRT) under the framework of hypothesis testing theory. Extensive experiments empirically verify our proposed multiple discriminators outperform many prior arts. Furthermore, the robustness of discriminators is validated with considering some post-processing attacks.
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