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.

提出一种 Progressive Spatio-Channel Correlation Network(PSCC-Net)框架来检测和定位图像篡改，结合多尺度、特征交叉和局部全局特征提取，以及 Spatio-Channel Correlation Module(SCCM)综合特征等技术，PSCC-Net 在图像篡改检测和定位方面的表现优于现有算法。

PSCC-Net: 渐进式空间-通道相关网络用于图像篡改检测和定位