  Deep learning (DL) has demonstrated its powerful capabilities in the field of
image inpainting, which could produce visually plausible results. Meanwhile,
the malicious use of advanced image inpainting tools (e.g. removing key objects
to report fake news) has led to increasing threats to the reliability of image
data. To fight against the inpainting forgeries, in this work, we propose a
novel end-to-end Generalizable Image Inpainting Detection Network (GIID-Net),
to detect the inpainted regions at pixel accuracy. The proposed GIID-Net
consists of three sub-blocks: the enhancement block, the extraction block and
the decision block. Specifically, the enhancement block aims to enhance the
inpainting traces by using hierarchically combined special layers. The
extraction block, automatically designed by Neural Architecture Search (NAS)
algorithm, is targeted to extract features for the actual inpainting detection
tasks. In order to further optimize the extracted latent features, we integrate
global and local attention modules in the decision block, where the global
attention reduces the intra-class differences by measuring the similarity of
global features, while the local attention strengthens the consistency of local
features. Furthermore, we thoroughly study the generalizability of our
GIID-Net, and find that different training data could result in vastly
different generalization capability. Extensive experimental results are
presented to validate the superiority of the proposed GIID-Net, compared with
the state-of-the-art competitors. Our results would suggest that common
artifacts are shared across diverse image inpainting methods. Finally, we build
a public inpainting dataset of 10K image pairs for the future research in this
area.
