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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 theimage in a two-path procedure: a top-down path that extracts local and globalfeatures and a bottom-up path that detects whether the input image ismanipulated, and estimates its manipulation masks at multiple scales, whereeach mask is conditioned on the previous one. Different from the conventionalencoder-decoder and no-pooling structures, PSCC-Net leverages features atdifferent scales with dense cross-connections to produce manipulation masks ina 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 awide range of manipulation attacks. Thanks to the light-weight backbone andprogressive mechanism, PSCC-Net can process 1,080P images at 50+ FPS. Extensiveexperiments demonstrate the superiority of PSCC-Net over the state-of-the-artmethods on both detection and localization.

PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization