Multi-Task Wavelet Corrected Network for Image Splicing Forgery Detection and Localization

Although the existing image splicing forgery detection networks can achieve a promising performance, most of these networks utilize regular pooling operations (max-pooling and mean-pooling) and a single task strategy, which limits the comprehensiveness and representativeness of the features learned by the networks. In this paper, we propose a multi-task wavelet corrected network (MWC-Net) that can learn more comprehensive and representative features for image splicing forgery detection and localization. MWC-Net exploits wavelet-pooling and wavelet un-pooling to compress and reconstruct the features of splicing forgery images, which can reduce information loss during learning features. Mean-while, MWC-Net implements a multi-task strategy to improve its ability to learn and utilize more comprehensive and representative features. The experimental results demonstrate that MWC-Net outperforms the state-of-the-art methods in splicing forgery detection and localization on four public datasets.