Junyi Cao · Ke-Yue Zhang · Taiping Yao · Shouhong Ding · Xiaokang Yang · Chao Ma

We propose a dual space reconstruction framework to detect face forgery and spoofing attacks simultaneously by focusing on the commonalities of real faces in spatial and frequency domains. In addition, we release the UniAttack benchmark, which covers both digital manipulations and physical presentation attacks, to evaluate the performance of face attack detectors in a more realistic scenario.

Please consider citing our paper if you find it interesting or helpful to your research.

@article{Cao_2024_IJCV,
  author    = {Cao, Junyi and Zhang, Ke-Yue and Yao, Taiping and Ding, Shouhong and Yang, Xiaokang and Ma, Chao},
  title     = {Towards Unified Defense for Face Forgery and Spoofing Attacks via Dual Space Reconstruction Learning},
  journal   = {International Journal of Computer Vision (IJCV)},
  year      = {2024},
  pages     = {1--26},
  publisher = {Springer}
}

@inproceedings{Cao_2022_CVPR,
    author    = {Cao, Junyi and Ma, Chao and Yao, Taiping and Chen, Shen and Ding, Shouhong and Yang, Xiaokang},
    title     = {End-to-End Reconstruction-Classification Learning for Face Forgery Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {4113-4122}
}

Please ensure that you have already installed the following packages.

• Pytorch 1.12.1
• Torchvision 0.13.1
• Albumentations 1.4.2
• Timm 0.9.16
• WandB 0.16.3
• Scipy 1.10.1
• Scikit-learn 1.3.2
• PyYaml 6.0.1
• OpenCV 4.9.0.80
• Lmdb 1.4.1
• Tqdm 4.66.2

• We include the dataset loaders and training engines for
  • Face Forgery Detection Benchmarks: FaceForensics++, Celeb-DF, and WildDeepfake.
  • Face Anti-Spoofing Benchmark: OCIM (including OULU-NPU, CASIA-MFSD, replay-attack, and MSU-MFSD).
  • UniAttack Benchmark.
• For face forgery detection and face anti-spoofing benchmarks, please follow their official dataset papers to download and process the data.

• Our pre-processed UniAttack benchmark data (in lmdb format) could be accessed through OneDrive with password b,Y5m>gp.
• Brief description (please refer to Section 4 of the manuscript for details):
  • Motivation · In real-world scenarios, the integrity of deployed face recognition systems is threatened by both digitally manipulated face forgeries and physically crafted spoofing samples. UniAttack is thus proposed to evaluate model performance on the unified detection of face forgery and spoofing attacks.
  • Data Composition · UniAttack consists of 6 existing datasets. Three datasets are used for face forgery attacks: FaceForensics++, Celeb-DF, and Seq-DeepFake. Three datasets are used for face spoofing attacks: HQ-WMCA, OULU-NPU, and SiW-Mv2.
  • Protocols · UniAttack consists of 3 evaluation protocols: Protocol I evaluates intra-dataset performance; Protocol II evaluates cross-dataset performance; Protocol III evaluates cross-attack-type performance.

• Please first check the format of the configuration templates provided here. Then, copy the templates into a new folder by:

  cp -r config_template config

• Please create a new folder ckpt and download imagenet pre-trained weights of EfficientNet-b4, ResNet18, and ResNet50 inside ckpt.

  mkdir ckpt && cd ckpt
  wget https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth
  wget https://download.pytorch.org/models/resnet18-5c106cde.pth
  wget https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth

• To start a training experiment, run the following script in your console.

  CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port 10000 \
    main.py \
    --config path/to/model_config.yml \
    --engine $ENGINE

  • --config: Specify the path of the config file of a model (i.e., the file name starts with a model_ from the provided templates).
  • --engine: Choose from FE (for face forgery detection), OCIM (for face anti-spoofing), or UE (for UniAttack benchmark).
  • Run python main.py --help to see all command line arguments.
• A concrete example to train our model on FaceForensics++:

  CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port 10000 \
    main.py \
    --config config/forgery/model_udeb4.yml \
    --engine FE

• A concrete example to train our model on OCIM:

  CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port 10000 \
    main.py \
    --config config/ocim/model_udr18.yml \
    --engine OCIM

• A concrete example to train our model on UniAttack:

  CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port 10000 \
    main.py \
    --config config/uniatt/Prot1/model_udeb4.yml \
    --engine UE

• To test a model, run the following script in your console.

  python main.py --config path/to/model_config.yml --engine $ENGINE -r $GPU --test 

  • --config: Specify the path of the config file.
  • -r: Specify the GPU index to run the experiment.