  To defend against manipulation of image content, such as splicing, copy-move,
and removal, we develop a Progressive Spatio-Channel Correlation Network
(PSCC-Net) to detect and localize image manipulations. PSCC-Net processes the
image in a two-path procedure: a top-down path that extracts local and global
features and a bottom-up path that detects whether the input image is
manipulated, and estimates its manipulation masks at 4 scales, where each mask
is conditioned on the previous one. Different from the conventional
encoder-decoder and no-pooling structures, PSCC-Net leverages features at
different scales with dense cross-connections to produce manipulation masks in
a coarse-to-fine fashion. Moreover, a Spatio-Channel Correlation Module (SCCM)
captures both spatial and channel-wise correlations in the bottom-up path,
which endows features with holistic cues, enabling the network to cope with a
wide range of manipulation attacks. Thanks to the light-weight backbone and
progressive mechanism, PSCC-Net can process 1,080P images at 50+ FPS. Extensive
experiments demonstrate the superiority of PSCC-Net over the state-of-the-art
methods on both detection and localization.
