Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution

TL;DR

This paper introduces an adversarial learning framework with atrous convolution-based deep matching for improved image splicing detection and localization, achieving superior results on multiple datasets.

Contribution

The paper proposes a novel adversarial training framework for CISDL using a deep matching network with atrous convolution and hierarchical feature extraction.

Findings

Effective detection of spliced regions demonstrated on 21 generated sets.

Superior performance compared to existing methods.

Robust localization of tampered regions at multiple scales.

Abstract

Constrained image splicing detection and localization (CISDL) is a newly proposed challenging task for image forensics, which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other. In this paper, we propose a novel adversarial learning framework to train the deep matching network for CISDL. Our framework mainly consists of three building blocks: 1) the deep matching network based on atrous convolution (DMAC) aims to generate two high-quality candidate masks which indicate the suspected regions of the two input images, 2) the detection network is designed to rectify inconsistencies between the two corresponding candidate masks, 3) the discriminative network drives the DMAC network to produce masks that are hard to distinguish from ground-truth ones. In DMAC, atrous convolution is adopted to extract features with rich spatial information, the correlation layer based on the skip architecture is proposed to capture hierarchical features, and atrous spatial pyramid pooling is constructed to localize tampered regions at multiple scales. The detection network and the discriminative network act as the losses with auxiliary parameters to supervise the training of DMAC in an adversarial way. Extensive experiments, conducted on 21 generated testing sets and two public datasets, demonstrate the effectiveness of the proposed framework and the superior performance of DMAC.