  Detecting manipulated images has become a significant emerging challenge. The
advent of image sharing platforms and the easy availability of advanced photo
editing software have resulted in a large quantities of manipulated images
being shared on the internet. While the intent behind such manipulations varies
widely, concerns on the spread of fake news and misinformation is growing.
Current state of the art methods for detecting these manipulated images suffers
from the lack of training data due to the laborious labeling process. We
address this problem in this paper, for which we introduce a manipulated image
generation process that creates true positives using currently available
datasets. Drawing from traditional work on image blending, we propose a novel
generator for creating such examples. In addition, we also propose to further
create examples that force the algorithm to focus on boundary artifacts during
training. Strong experimental results validate our proposal.
