  We consider an information theoretic approach to address the problem of
identifying fake digital images. We propose an innovative method to formulate
the issue of localizing manipulated regions in an image as a deep
representation learning problem using the Information Bottleneck (IB), which
has recently gained popularity as a framework for interpreting deep neural
networks. Tampered images pose a serious predicament since digitized media is a
ubiquitous part of our lives. These are facilitated by the easy availability of
image editing software and aggravated by recent advances in deep generative
models such as GANs. We propose InfoPrint, a computationally efficient solution
to the IB formulation using approximate variational inference and compare it to
a numerical solution that is computationally expensive. Testing on a number of
standard datasets, we demonstrate that InfoPrint outperforms the
state-of-the-art and the numerical solution. Additionally, it also has the
ability to detect alterations made by inpainting GANs.
