  Deep image inpainting aims to restore damaged or missing regions in an image
with realistic contents. While having a wide range of applications such as
object removal and image recovery, deep inpainting techniques also have the
risk of being manipulated for image forgery. A promising countermeasure against
such forgeries is deep inpainting detection, which aims to locate the inpainted
regions in an image. In this paper, we make the first attempt towards universal
detection of deep inpainting, where the detection network can generalize well
when detecting different deep inpainting methods. To this end, we first propose
a novel data generation approach to generate a universal training dataset,
which imitates the noise discrepancies exist in real versus inpainted image
contents to train universal detectors. We then design a Noise-Image
Cross-fusion Network (NIX-Net) to effectively exploit the discriminative
information contained in both the images and their noise patterns. We
empirically show, on multiple benchmark datasets, that our approach outperforms
existing detection methods by a large margin and generalize well to unseen deep
inpainting techniques. Our universal training dataset can also significantly
boost the generalizability of existing detection methods.
