Capsule-Forensics-v2

Implementation of the paper: Use of a Capsule Network to Detect Fake Images and Videos, which is an updated version of the previous work: Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos (ICASSP 2019).

You can clone this repository into your favorite directory:

$ git clone https://github.com/nii-yamagishilab/Capsule-Forensics-v2

1. Requirement

• PyTorch 1.3
• TorchVision
• scikit-learn
• Numpy

2. Project organization

• Databases folder, where you can place your training, evaluation, and test set:

  ./databases/<faceforensicspp; cgvsphoto_patches; cgvsphoto_full; replay_attack>/<train; validation; test>
  

• Checkpoint folder, where the training outputs will be stored:

  ./checkpoints/<binary_faceforensicspp; multiclass_faceforensicspp; cgvsphoto; replay_attack>
  

Pre-trained models for the FaceForensics++ database (includes Real, DeepFakes, Face2Face, and FaceSwap), the CGvsPhoto database, and the Replay-Attack database (with settings described in our paper) are provided in the checkpoints folder.

Pre-trained models for the FaceForensics++ database which includes NeuralTextures will be availabe soon.

3. Databases

In case of the FaceForensics++ database, it need to be pre-processed to crop facial area. We recommend using an image size of 300 x 300 as the input.

4. Training

Note: Parameters with detail explanation could be found in the corresponding source code.

Training the Capsule-Forensics-v2 using binary classification on the FaceForensics++ database:

$ python train_binary_ffpp.py

Training the Capsule-Forensics-v2 using multiclass classification on the FaceForensics++ database:

$ python train_multiclass_ffpp.py

Training the Capsule-Forensics-v2 on the CGvsPhoto database:

$ python train_cgvsphoto.py

Training the Capsule-Forensics-v2 on the Idiap Replay-Attack database:

$ python train_replay_attack.py

5. Evaluating

Note: Parameters with detail explanation could be found in the corresponding source code.

5.1. FaceForensics++ database (includes Real, DeepFakes, Face2Face, and FaceSwap)

Binary classification on images:

$ python test_binary_ffpp.py

Binary classification on videos (extracted as frames):

$ python test_vid_binary_ffpp.py

Multiclass classification on images:

$ python test_multiclass_ffpp.py

Multiclass classification on images with detail results on each class:

$ python test_multiclass_detail_ffpp.py

Multiclass classification on videos (extracted as frames):

$ python test_vid_multiclass_ffpp.py

5.2. CGvsPhoto database

Testing on patches:

$ python test_cgvsphoto.py

Testing on full images:

$ python test_cgvsphoto_full.py

5.3. Idiap Replay-Attack database

Testing on images:

$ python test_replay_attack.py

6. Authors

• Huy H. Nguyen (https://researchmap.jp/nhhuy/?lang=english)
• Junichi Yamagishi (https://researchmap.jp/read0205283/?lang=english)
• Isao Echizen (https://researchmap.jp/echizenisao/?lang=english)

Acknowledgement

This research was supported by JSPS KAKENHI Grants JP16H06302 and JP18H04120 and by JST CREST Grant JPMJCR18A6, Japan.

Reference

H. H. Nguyen, J. Yamagishi, and I. Echizen, “Use of a Capsule Network to Detect Fake Images and Videos,” arXiv preprint arXiv:1910.12467. 2019 Oct 29.