We formulate PRNU-based image manipulation localization as a probabilistic binary labeling task in a flexible discriminative random field (DRF) framework. A novel local discriminator based on the deviation of the measured correlation from the expected local correlation as estimated by a correlation predictor is paired with an explicit pairwise model for dependencies between local decisions. Experimental results from the Dresden Image Database indicate that the DRF outperforms prior art with Markov random field label priors.