Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization

Abstract

Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task that aims at detecting and localizing the source and forged regions from a series of input suspected image pairs. In this work, we propose a novel Scale-Adaptive Deep Matching (SADM) network for CISDL, consisting of a feature extractor, a scale-adaptive correlation module and a novel mask generator. The feature extractor is built on VGG, which has been reconstructed with atrous convolution. In the scale-adaptive correlation computation module, squeeze-and-excitation (SE) blocks and truncation operations are integrated to process arbitrary-sized images. In the mask generator, an attention-based separable convolutional block is designed to reconstruct richer spatial information and generate more accurate localization results with less parameters and computation burden. Last but not least, we design a pyramid framework of SADM to capture multiscale details, which can increase the detection and localization accuracy of multiscale regions and boundaries. Extensive experiments demonstrate the effectiveness of SADM and the pyramid framework.