To counter the ever increasing flood of image forgeries in the form of spliced images in social media and the web in general, we propose the novel image splicing localization CNN NoiseSeg. NoiseSeg fuses statistical and CNN-based splicing localization methods in separate branches to leverage the benefits of both. Unique splicing anomalies that can be identified by its coarse noise separation branch, fine-grained noise feature branch and error level analysis branch all get combined in a segmentation fusion head to predict a precise localization of the spliced regions. Experiments on the DSO-1, CASIAv2, DEFACTO, IMD2020 and WildWeb image splicing datasets show that NoiseSeg outperforms most other state-of-the-art methods significantly and even up to a margin of 46.8%.