  Deep models have been widely and successfully used in image manipulation
detection, which aims to classify tampered images and localize tampered
regions. Most existing methods mainly focus on extracting global features from
tampered images, while neglecting the relationships of local features between
tampered and authentic regions within a single tampered image. To exploit such
spatial relationships, we propose Proposal Contrastive Learning (PCL) for
effective image manipulation detection. Our PCL consists of a two-stream
architecture by extracting two types of global features from RGB and noise
views respectively. To further improve the discriminative power, we exploit the
relationships of local features through a proxy proposal contrastive learning
task by attracting/repelling proposal-based positive/negative sample pairs.
Moreover, we show that our PCL can be easily adapted to unlabeled data in
practice, which can reduce manual labeling costs and promote more generalizable
features. Extensive experiments among several standard datasets demonstrate
that our PCL can be a general module to obtain consistent improvement. The code
is available at https://github.com/Sandy-Zeng/PCL.
