  The amount of digital imagery recorded has recently grown exponentially, and
with the advancement of software, such as Photoshop or Gimp, it has become
easier to manipulate images. However, most images on the internet have not been
manipulated and any automated manipulation detection algorithm must carefully
control the false alarm rate. In this paper we discuss a method to
automatically detect local resampling using deep learning while controlling the
false alarm rate using a-contrario analysis. The automated procedure consists
of three primary steps. First, resampling features are calculated for image
blocks. A deep learning classifier is then used to generate a heatmap that
indicates if the image block has been resampled. We expect some of these blocks
to be falsely identified as resampled. We use a-contrario hypothesis testing to
both identify if the patterns of the manipulated blocks indicate if the image
has been tampered with and to localize the manipulation. We demonstrate that
this strategy is effective in indicating if an image has been manipulated and
localizing the manipulations.
