RGB-N

Code and synthetic dataset generation for the CVPR 2018 paper "Learning Rich Features for Image Manipulation Detection"

Environment

tensorflow 0.12.1, python3.5.2, cuda 8.0.44 cudnn 5.1

Other packages please run:

pip install -r requirements.txt

Compile lib and compact_bilinear_pooling:

1. Check if the cuda lib path in compact_bilinear_pooling/sequential_fft/complie.sh is correct.

2. Run the command:

cd lib
make
cd compact_bilinear_pooling/sequential_fft
./compile.sh

For more detail, see https://github.com/ronghanghu/tensorflow_compact_bilinear_pooling

Pre-trained model

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

Synthetic dataset

1. Download COCO 2014 dataset (http://cocodataset.org/#download) and COCO PythonAPI (https://github.com/cocodataset/cocoapi) and put in coco_synthetic folder. After this step the coco dataset folder 'cocostuff' will be created.
2. Change dataDir in coco_synthetic/demo.py to the path of 'train2014' (e.g, ./cocostuff/coco/train2014)
3. Run run_demo.sh 1 100 to choose the begin and end COCO category used for creating the tamper synthetic dataset.
4. Run split_train_test.py to make train/test split. (making sure that the images used to generate training set not overlap with the images for testing)

Train on synthetic dataset

1. Change the coco synthetic path in lib/factory.py:

coco_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:

        python3 ./tools/trainval_net.py \
            --weight /path of res101.ckpt/data/imagenet_weights/res101.ckpt \ #FIXME
            --imdb ${TRAIN_IMDB} \
            --imdbval ${TEST_IMDB} \
            --iters ${ITERS} \
            --cfg cfgs/${NET}.yml \
            --net ${NET} \
            --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS}

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

./train_faster_rcnn.sh 0 coco res101_fusion EXP_DIR coco_flip_0001_bilinear_new

Use synthetic pre-trained model for fine tuning

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

        python3 ./tools/trainval_net.py \
            --weight /path of synthetic pretrain model/res101_fusion_faster_rcnn_iter_60000.ckpt \ #FIXME
            --imdb ${TRAIN_IMDB} \
            --imdbval ${TEST_IMDB} \
            --iters ${ITERS} \
            --cfg cfgs/${NET}.yml \
            --net ${NET} \
            --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS}

2. Specify the dataset, gpu, and network in train_dist_faster.sh as below as run the file (use NIST as an example)

./train_faster_rcnn.sh 0 NIST res101_fusion EXP_DIR NIST_flip_0001_bilinear_new

Test the model

1. Check the model path match well with NET_FINAL in test_faster_rcnn.sh, making sure the checkpoint iteration exist in model output path. Otherwise, change the iteration number ITERS as needed.

  coco)
    TRAIN_IMDB="coco_train_filter_single"
    TEST_IMDB="coco_test_filter_single"
    ITERS=60000
    ANCHORS="[8,16,32,64]"
    RATIOS="[0.5,1,2]"
    ;;

2. Run test_dist_faster.sh. If things go correcty, it should print out MAP and save tamper.txt and tamper.png indicating the detection result and PR curve.

Synthetic dataset and training/testing split

https://drive.google.com/open?id=1vIAFsftjmHg2J5lJgO92C1Xmyw539p_B

Citation:

If this code or dataset helps your research, please cite our paper:

@inproceedings{zhou2018learning,
  title={Learning Rich Features for Image Manipulation Detection},
  author={Zhou, Peng and Han, Xintong and Morariu, Vlad I and Davis, Larry S},
  booktitle = {CVPR},
  year={2018}
}