We present PRNU-based image manipulation localization as a probabilistic labeling task in a flexible discriminative random field (DRF) setup. Instead of reaching local decisions independent of each other, discriminative random fields incorporate local inter-label dependencies while keeping the formulation general enough to make label assignments depend on both local and non-local image characteristics. With an improved form of association potential combining normalized correlation and the deviation of the measured correlation from the expected correlation and an interaction potential defined as the weighted L2 norm squared between intensities of neighboring pixels, we were able to localize even considerably small manipulations on realistic tampered images. We experimented with different combinations of window sizes to capture features to predict the correlation more accurately than already existing algorithms. Experimental results indicate that our algorithm outperforms recent state of the art methods based on multiscale analysis strategies. We also found that for inspecting manipulated images which are JPEG compressed, it helps to train the predictor with JPEG images rather than with uncompressed images and for all quality factors, it is possible to work with two predictors, one trained for images with lower quality factors and another for higher quality factors.