CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

(Accepted for oral presentation at ACMMM '21)

Paper Link: (arXiv) (ACMMM version)

Overview

We propose Continual Representation using Distillation (CoReD) method that employs the concept of Continual Learning (CL), Representation Learning (RL), and Knowledge Distillation (KD).

Comparison Baselines

• Transfer-Learning (TL) : The first method is Transfer learning, where we perform fine-tuning on the model to learning the new Task.
• Distillaion Loss (DL) : The third method is a part of our ablation study, wherewe only use the distillation loss component from our CoReD loss function to perform incremental learning.
• Transferable GAN-generated Images Detection Framewor (TG) : The second method is a KD-based GAN image detection framework using L2-SP and self-training.

Training & Evaluation

- Requirements and Installation

We recommend the installation using the requilrements.txt contained in this Github.

python==3.8.0
torchvision==0.9.1
torch==1.8.1
sklearn
numpy
opencv-python

pip install -r requirements.txt

• Note that :

- Train & Evaluation

- Full Usages

  -m                   Model name = [CoReD, KD, TG, FT]
  -n                   Network name = [Xception, Efficient']
  -te                  Turn on test mode [True, False]
    
  --name_sources       Name of 'Source' datasets. one or multiple names. (ex. DeepFake / DeepFake_Face2Face / DeepFake_Face2Face_FaceSwap)
  --name_target        Name of 'Target' dataset. only a single name. (ex.DeepFake / Face2Face / FaceSwap / NeuralTextures) / used for Train only')
  --data          Dataset path. it must be contained Sources & Target folder name
  --weigiht     You can select the full path or folder path included in the '.pth' file
    
  -lr                  Learning late (For training)
  -a                   Alpha of KD-Loss
  -nc                  Number of Classes
  -ns                  Number of Stores
  -me                  Number of Epoch (For training)
  -nb                  Batch-Size
  -ng                  GPU-device can be set as ei 0,1,2 for multi-GPU (default=0) 

- Train

To train and evaluate the model(s) in the paper, run this command:

• Task1 We must train pre-trained single model for task1 .

  python main.py -t={Source Name} -d={folder_path} -w={weights}  
  python main.py -t=DeepFake -d=./mydrive/dataset/' #Example 
  

• Task2 - 4

  python main.py -s={Source Name} -t={Target Name} -d={folder_path} -w={weights}  
  python main.py -s=Face2Face_DeepFake -t=FaceSwap -d=./mydrive/dataset/ -w=./weights' #Example
  

• Note that If you set -s=Face2Face_DeepFake -t=FaceSwap -d=./mydrive/dataset -w=./weights when you start training, data path "./mydrive/dataset" must include 'Face2Face', 'DeepFake', and 'FaceSwap', and these must be contained the 'train','val' folder which include 'real'&'fake' folders.

- Evaluation

After train the model, you can evaluate the dataset.

• Eval

  python main.py -d={dataset as full name} -w={weights} --test  
  python main.py -d=./mydrive/dataset/DeepFake/testset -w=./weights/bestmodel.pth --test #Example
  

- Result

• AUC scores (%) of various methods on compared datasets.

- Task1 (GAN datasets and FaceForensics++ datasets)

- Task2 - 4

Citation

If you find our work useful for your research, please consider citing the following papers :)

@misc{kim2021cored,
    title={CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation},
    author={Minha Kim and Shahroz Tariq and Simon S. Woo},
    year={2021},
    eprint={2107.02408},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

- Contect

If you have any questions, please contact us at kimminha/[email protected]

- License

The code is released under the MIT license. Copyright (c) 2021