  Image cropping can be maliciously used to manipulate the layout of an image
and alter the underlying meaning. Previous image crop detection schemes only
predicts whether an image has been cropped, ignoring which part of the image is
cropped. This paper presents a novel robust watermarking network (RWN) for
image crop localization. We train an anti-crop processor (ACP) that embeds a
watermark into a target image. The visually indistinguishable protected image
is then posted on the social network instead of the original image. At the
recipient's side, ACP extracts the watermark from the attacked image, and we
conduct feature matching on the original and extracted watermark to locate the
position of the crop in the original image plane. We further extend our scheme
to detect tampering attack on the attacked image. Besides, we explore a simple
yet efficient method (JPEG-Mixup) to improve the generalization of JPEG
robustness. According to our comprehensive experiments, RWN is the first to
provide high-accuracy and robust image crop localization. Besides, the accuracy
of tamper detection is comparable with many state-of-the-art passive-based
methods.
