2D3MF: Deepfake Detection using Multi Modal Middle Fusion

[!CAUTION] This repo is under development. No hyper parameter tuning is presented yet here; hence, the current architecture is not optimal for deepfake detection.

This repo is the implementation for the paper 2D3MF: Deepfake Detection using Multi Modal Middle Fusion.

Repository Structure

.
├── assets                # Images for README.md
├── LICENSE
├── README.md
├── MODEL_ZOO.md
├── CITATION.cff
├── .gitignore
├── .github

# below is for the PyPI package marlin-pytorch
├── src                   # Source code for marlin-pytorch and audio feature extractors
├── tests                 # Unittest
├── requirements.lib.txt
├── setup.py
├── init.py
├── version.txt

# below is for the paper implementation
├── configs              # Configs for experiments settings
├── TD3MF                # 2D3MF model code
├── preprocess           # Preprocessing scripts
├── dataset              # Dataloaders
├── utils                # Utility functions
├── train.py             # Training script
├── evaluate.py          # Evaluation script
├── requirements.txt

Installing and running our model

Feature Extraction - 2D3MF

Install 2D3MF from pypi

pip install 2D3MF

Sample code snippet for feature extraction

from TD3MF.classifier import TD3MF
ckpt = "ckpt/celebvhq_marlin_deepfake_ft/last-v72.ckpt"
model = TD3MF.load_from_checkpoint(ckpt)
features = model.feature_extraction("2D3MF_Datasets/test/SampleVideo_1280x720_1mb.mp4")

We have some pretrained marlin checkpoints and configurations [here]()

Paper Implementation

Requirements:

• Python >= 3.7, < 3.12
• PyTorch ~= 1.11
• Torchvision ~= 0.12
• ffmpeg

Installation

Install PyTorch from the official website

Clone the repo and install the requirements:

git clone https://github.com/aiden200/2D3MF
cd 2D3MF
pip install -e .

Training

1. Download Datasets

2. Preprocess the dataset

We recommend using the following unified dataset structure

2D3MF_Dataset/
├── DeepfakeTIMIT
│   ├── audio/*.wav
│   └── video/*.mp4
├── DFDC
│   ├── audio/*.wav
│   └── video/*.mp4
├── FakeAVCeleb
│   ├── audio/*.wav
│   └── video/*.mp4
├── Forensics++
│   ├── audio/*.wav
│   └── video/*.mp4
├── RAVDESS
    ├── audio/*.wav
    └── video/*.mp4

Crop the face region from the raw video. Run:

python3 preprocess/preprocess_clips.py --data_dir [Dataset_Dir]

3. Extract features from pretrained models

Run:

python preprocess/extract_features.py --data_dir /path/to/data --video_backbone [VIDEO_BACKBONE] --audio_backbone [AUDIO_BACKBONE]

[VIDEO_BACKBONE] can be replaced with one of the following:

• marlin_vit_small_ytf
• marlin_vit_base_ytf
• marlin_vit_large_ytf
• efficientface

[AUDIO_BACKBONE] can be replaced with one of the following:

• MFCC
• xvectors
• resnet
• emotion2vec
• eat

Optionally add the --Forensics flag in the end if Forensics++ is the dataset being processed.

From our paper, we found that eat works the best as the audio backbone.

Split the train val and test sets. Run:

python preprocess/gen_split.py --data_dir /path/to/data --test 0.1 --val 0.1 --feat_type [AUDIO_BACKBONE]

Note that the pre-trained video_backbone and audio_backbone can be downloaded from MODEL_ZOO.md

4. Train and evaluate

Train and evaluate the 2D3MF model..

Please use the configs in config/*.yaml as the config file.

python evaluate.py \
    --config /path/to/config \
    --data_path /path/to/CelebV-HQ
    --num_workers 4
    --batch_size 16

python evaluate.py \
    --config /path/to/config  \
    --data_path /path/to/dataset \
    --num_workers 4 \
    --batch_size 8 \
    --marlin_ckpt pretrained/marlin_vit_base_ytf.encoder.pt \
    --epochs 300

python evaluate.py --config config/celebvhq_marlin_deepfake_ft.yaml --data_path 2D3MF_Datasets --num_workers 4     --batch_size 1 --marlin_ckpt pretrained/marlin_vit_small_ytf.encoder.pt --epochs 300

Optionally, add

--skip_train --resume /path/to/checkpoint

To skip the training.

5. Configuration File

Set a configuration file based on your hyperparameters and backbones. You can find a example config file under config/

Explanation:

• training_datasets - list, can contain one or more datasets within "DeepfakeTIMIT", "RAVDESS", "Forensics++", "DFDC", "FakeAVCeleb"
• eval_datasets- list, can contain one or more datasets within "DeepfakeTIMIT", "RAVDESS", "Forensics++", "DFDC", "FakeAVCeleb"
• learning_rate - int, ex: 1.00e-3
• num_heads - int, Number of attention heads
• fusion - str, Choice of fusion type: "mf" for middle fusion and "lf" for late fusion.
• audio_positional_encoding - bool, add audio positional encoding
• hidden_layers - int, hidden layers
• lp_only - bool, setting this to be true will perform inference from the video features only
• audio_backbone- str, select one of the following options: "MFCC", "eat", "xvectors", "resnet", "emotion2vec"
• middle_fusion_type- str, select one of the following options: "default", "audio_refuse", "video_refuse", "self_attention", "self_cross_attention"
• modality_dropout - float, modality dropout rate
• video_backbone - str, select one of the following options: "efficientface", "marlin"

6. Performing Grid Search

• config/grid_search_config.py
• --grid_search

7. Monitoring Performance:

Run

tensorboard --logdir=lightning_logs/

Should be hosted on http://localhost:6006/

License

This project is under the CC BY-NC 4.0 license. See LICENSE for details.

References

Please cite our work!

Acknowledgements

Some code about model is based on ControlNet/MARLIN. The code related to middle fusion is from Self-attention fusion for audiovisual emotion recognition with incomplete data.

Our Audio Feature Extraction Models:

• EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
• X-Vectors
• Resnet
• Emotion2vec

Our Video Feature Extraction Models:

• MARLIN
• EfficientFace