CISDL-DMAC

Constrained image splicing detection and localization (CISDL), which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other, is a newly proposed challenging task for image forensics. We propose a novel adversarial learning framework to learn a deep matching network for CISDL. Our framework mainly consists of three building blocks: 1) A deep matching network based on atrous convolution (DMAC) aims to generate two high-quality candidate masks which indicate suspected regions of the two input images. In DMAC, atrous convolution is adopted to extract features with rich spatial information, a correlation layer based on a skip architecture is proposed to capture hierarchical features, and atrous spatial pyramid pooling is constructed to localize tampered regions at multiple scales. 2) A detection network is designed to rectify inconsistencies between the two corresponding candidate masks. 3) A discriminative network drives the DMAC network to produce masks that are hard to distinguish from ground-truth ones. The detection network and the discriminative network collaboratively supervise the training of DMAC in an adversarial way. Besides, a sliding window based matching strategy is investigated for high-resolution images matching.

Citation

The code is for Research Use Only. If you use this code for your research, please cite our paper.

@article{liuyaqi2019tifs,
  title={Adversarial Learning for Constrained Image Splicing Detection and Localization based on Atrous Convolution},
  author={Liu, Yaqi and Zhu, Xiaobin and Zhao, Xianfeng and Cao, Yun},
  journal={{IEEE} Trans. Inf. Forensics Security},
  volume={14},
  number={10},
  pages={2551--2566},
  year={2019}
}

We have proposed an improved version named as AttentionDM. You can kindly refer to our paper.

@article{liuyaqi2020access,
  title={Constrained Image Splicing Detection and Localization With Attention-Aware Encoder-Decoder and Atrous Convolution},
  author={Liu, Yaqi and Zhao, Xianfeng},
  journal={IEEE Access},
  volume={8},
  pages={6729--6741},
  year={2020},
  publisher={IEEE}
}

Getting Started

We provide PyTorch implementations for both DMAC and DMVN.

The code was written and supported by Yaqi Liu.

Prerequisites

• Linux
• Python 2
• NVIDIA GPU + CUDA

Installation

• Install PyTorch 0.4+ and torchvision from http://pytorch.org and OpenCV 3.

• Clone this repo:

git clone https://github.com/yaqiliu-cs/CISDL-DMAC
cd CISDL-DMAC

Pre-trained models

Download our pre-trained DMAC-adv and DMVN-BN models.

DMAC demo

• You can run a simple demo:

python demo.py im1_1.jpg im1_2.jpg

DMAC train

• Training Data Preparation

You can generate sufficient synthetic image pairs for training by running "combine_generation_train.m" based on the MatlabAPI of the MS COCO dataset.

Changing training paths and lists to your generated training sets, you can train your own DMAC and DMAC-adv models as follows:

• Training based on the single spatial cross-entropy loss

python train_ce_script.py

• Optimizing based on the multi-task loss

python train_adversary_script.py

Generated testing sets

• Combination Sets.
• Generated sets for invariance analyses.

DMAC test

Download our generated Combination Sets.

• Test DMAC-adv model

python test_coco.py

• Test DMAC-adv model with a sliding window based matching strategy

python test_coco_slide.py

DMVN test

Download our generated Combination Sets.

• Test DMVN-BN model

python test_coco_dmvn.py

Authorship

Yaqi Liu

1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China.

E-mail: liuyaqi@iie.ac.cn