  Image forgery localization is a very active and open research field for the
difficulty to handle the large variety of manipulations a malicious user can
perform by means of more and more sophisticated image editing tools. Here, we
propose a localization framework based on the fusion of three very different
tools, based, respectively, on sensor noise, patch-matching, and machine
learning. The binary masks provided by these tools are finally fused based on
some suitable reliability indexes. According to preliminary experiments on the
training set, the proposed framework provides often a very good localization
accuracy and sometimes valuable clues for visual scrutiny.
