Sandy-Zeng / PCL

code for proposal contrastive learning

Proposal Contrastive Learning

Code for the paper "Towards Effective Image Manipulation Detection with Proposal Contrastive Learning"

Environment

tensorflow 0.12.1, python3.5.2, cuda 8.0.44 cudnn 5.1

As the codebase is adopted from RGB-N (Learning Rich Features for Image Manipulation Detection) the requirements of the environment and the sythetic dataset can be found from https://github.com/pengzhou1108/RGB-N).

Pre-trained model

For ImageNet resnet101 pre-trained model, please download from https://github.com/endernewton/tf-faster-rcnn

Train on synthetic dataset

1. Change the data path in lib/factory.py:

   coco_single_path= #FIXME
   for split in ['coco_train_filter_single', 'coco_test_filter_single']:
       name = split
       __sets[name] = (lambda split=split: coco(split,2007,coco_path))

2. Specify the ImageNet resnet101 pretrain model path in train_faster_rcnn.sh as below:

   WEIGHT_PATH= #FIXME

3. Specify the dataset, gpu, and network in train_dist_faster.sh as below as run the file

   (1) RGB-N

   ./train_faster_rcnn.sh 0 coco res101_fusion 0 EXP_DIR coco_res101_fusion

   (2) RGB-N+PCL

   ./train_faster_rcnn.sh 0 coco res101_contrastive 0 EXP_DIR coco_res101_contrastive

   (3) RGB-C

   ./train_faster_rcnn.sh 0 coco res101_constrained 0 EXP_DIR coco_res101_constrained

   (4) RGB-C+PCL

   ./train_faster_rcnn.sh 0 coco res101_constrained_ssl 0 EXP_DIR coco_res101_constrained_ssl

Use synthetic pre-trained model for fine tuning

Take NIST dataset for examples

Supervised learning Setting

1. Change the data path in lib/factory.py:

   nist_path= #FIXME
   for split in ['dist_NIST_train_new_2','dist_NIST_test_new_2']:
       name = split
       __sets[name] = (lambda split=split: nist(split,2007,nist_path))

2. Specify the RGB-C+PCL coco pretrain model path in train_faster_rcnn.sh as below:

   WEIGHT_PATH= #FIXME

3. Specify the dataset, gpu, and network in train_dist_faster.sh as below as run the file:

   RGB-C+PCL

   ./train_faster_rcnn.sh 0 NIST res101_constrained_ssl 0 EXP_DIR nist_res101_constrained_ssl
   

Semi-Supervised learning Setting

1. Set the label of unlabled data as "semi" and label of labeled data as "tamper"

   example data shown in ./data/NIST_train_new_2_semi.txt

2. Change the data path in lib/factory.py:

   nist_path= #FIXME
   for split in ['dist_NIST_train_new_2_semi', 'dist_NIST_test_new_2']:
       name = split
       __sets[name] = (lambda split=split: nist(split,2007,nist_path))

3. Specify the RGB-C+PCL coco pretrain model path in train_faster_rcnn.sh as below:

   WEIGHT_PATH= #FIXME

4. Specify the dataset, gpu, and network as below and run the command:

   RGB-C+PCL

   ./train_faster_rcnn.sh 0 NIST_unlabeled res101_constrained_ssl 0 EXP_DIR nist_res101_constrained_ssl_semi

Test the model

1. Check the model path match well, making sure the checkpoint iteration exist in model output path.

     coco)
       TRAIN_IMDB="coco_train_filter_single"
       TEST_IMDB="coco_test_filter_single"
       ITERS=110000
       ANCHORS="[8,16,32,64]"
       RATIOS="[0.5,1,2]"
       ;;

2. Specify the dataset, gpu, network and iters as below and run the command:

   ./test_faster_rcnn.sh 0 coco res101_constrained_ssl 110000 EXP_DIR coco_res101_constrained_ssl

Citation: