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In this paper, we propose a novel manipulation detection framework for image patches using a fusion procedure, called FusionBoost, in conjunction with accurately detected derivative correlation features. By first dividing all demosaiced samples of a color image into a number of categories, we estimate their underlying demosaicing formulas based on partial derivative correlation models and extract several types of derivative correlation features. The features are organized into small subsets according to both the demosaicing category and the feature type. For each subset, we train a lightweight manipulation detector using probabilistic support vector machines. FusionBoost is then proposed to learn the weights of an ensemble detector for achieving the minimum error rate. By applying the ensemble detector on cropped photo patches from different image sources, large-scale experiments show that our proposed method achieves low average detection error rates of 2.0% to 4.3% in simultaneously detecting a large variety of common manipulation attempts for image patches from several different source models. Our framework shows good learning efficiency for highly imbalanced tasks. In several patch-based detection examples, we demonstrate the efficacy of the proposed method in detecting image manipulations on local patches.