# Capsule-Forensics-v2

**Repository Path**: xia_zhi_ming/Capsule-Forensics-v2

## Basic Information

- **Project Name**: Capsule-Forensics-v2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-16
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Capsule-Forensics-v2

Implementation of the paper:  <a href="https://arxiv.org/abs/1910.12467">Use of a Capsule Network to Detect Fake Images and Videos</a>, which is an **updated version** of the previous work:  <a href="https://arxiv.org/abs/1810.11215">Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos</a> (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 <a href="https://github.com/nii-yamagishilab/Capsule-Forensics-v2/tree/master/checkpoints">checkpoints folder</a>.

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.