Fake news and deep fakes have been making social and mainstream media headlines. At the same time, engaged scientists strive for find- ing ways to detect forgeries and suspicious manipulations using even the subtlest clues. In this vein, this work proposes a new method for detecting photographic splicing by bringing together the high repre- sentation power of Illuminant Maps and Convolutional Neural Net- works as a way of learning directly from available training data the most important hints of a forgery. This work propose a method that eliminates the laborious feature engineering process, allow locate forgery region and yields a classification accuracy of more than 96%, outperforming state-of-the-art methods in different datasets. The po- tential uses of the proposed method is further highlighted by analyz- ing some suspicious real-world photographs that recently broke the news