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Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies

This repository contains the implementation of Audio Visual Deepfake Detection method proposed in the paper:

Marcella Astrid, Enjie Ghorbel, and Djamila Aouada, Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies, BMVC 2024.

Dependencies

Create conda environment with package inside the package list conda create -n myenv --file package-list.txt

Prepare data (DFDC)

  1. Download DFDC dataset from here.

  2. Store the train and test videos as follows:

    train/real/{videoname}.mp4  
train/fake/{videoname}.mp4  
test/real/{videoname}.mp4  
test/fake/{videoname}.mp4

    If you wish to use the same videos as used by the paper authors, please refer to train_fake_list.txt, train_real_list.txt, test_fake_list.txt, and test_real_list.txt.

  3. Once the videos have been placed at the above mentioned paths, run python pre-process.py --out_dir train and python pre-process.py --out_dir test for pre-processing the videos.

  4. After the above step, you can delete pyavi, pywork, pyframes and pycrop directories under train and test folders. (Do not delete pytmp folder please!)

  5. Collect video paths in csv files by running python write_csv.py --out_dir . command. Also can create the small set version (e.g., used in Table 3) with python write_csv.py --out_dir . --small

Prepare data (FakeAVCeleb)

  1. Download FakeAVCeleb dataset from here
  2. Run python preprocess_FakeAVCeleb_to_DFDCformat.py. See fakeavceleb_test_fake.txt and fakeavceleb_test_real.txt for list of videos we are using.
  3. Use instructions in Prepare data (DFDC). Adjust the --out_dir in step 3 respectively. Also add --dont_crop_face option in step 3. Use write_csv_fakeavceleb.py for step 5

Training

python my_train.py --out_dir . --epochs 100 --num_workers 7 --with_att --using_pseudo_fake

Remove --with_att for model without attention.

Remove --using_pseudo_fake for training without pseudo-fake.

Add --spatial_size 14 to change the spatial size to 14. Change the 14 to different number. (Figure 5(c) ablation)

Add --aud_min_fake_len 0.5 and --vis_min_fake_len 0.5 to change the minimum length of pseudo fake. (Figure 5(b) ablation). For ~0, change the 0.5 to 2 (the default value). Ratio value between 0 to 1. More than 1, it is regarded as number of frames instead

Similarly for the maximum length with --aud_max_fake_len 0.5 and --vis_max_fake_len 0.5. (Figure 5(a) ablation). Value -1 represent the maximum length (same to ratio = 1)

Testing

Final model weight file: drive

python my_train.py --out_dir . --test log_tmp/v5_mydf-natt1.0-upf-PF0,1,2_A5_afl2,-1_vfl2,-1_224_r18_bs8_lr0.0001/model/model_best_epoch99.pth.tar --with_att

Change the path of model file accordingly in the --test argument.

For computing AUC score, run python my_test.py --folder test_results/v5_mydf-natt1.0-upf-PF0,1,2_A5_afl2,-1_vfl2,-1_224_r18_bs8_lr0.0001/model_best_epoch99/imbalance after executing the above command, and see the result in test_results folder.

See the results in the output.txt inside test_results/v5_mydf-natt1.0-upf-PF0,1,2_A5_afl2,-1_vfl2,-1_224_r18_bs8_lr0.0001/model_best_epoch99/imbalance

For testing with FakeAVCeleb:

python my_train.py --out_dir . --test log_tmp/v5_mydf-natt1.0-upf-PF0,1,2_A5_afl2,-1_vfl2,-1_224_r18_bs8_lr0.0001/model/model_best_epoch99.pth.tar --with_att --dataset fakeavceleb
python my_test.py --folder test_results/v5_mydf-natt1.0-upf-PF0,1,2_A5_afl2,-1_vfl2,-1_224_r18_bs8_lr0.0001/model_best_epoch99/balance --dataset fakeavceleb

See the results in the output.txt inside test_results/v5_mydf-natt1.0-upf-PF0,1,2_A5_afl2,-1_vfl2,-1_224_r18_bs8_lr0.0001/model_best_epoch99/balance

Reference

If you use the code, please cite the paper -

@InProceedings{astrid2024statistics,
  author       = "Astrid, Marcella and Ghorbel, Enjie and Aouada, Djamila",
  title        = "Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies",
  booktitle    = "British Machine Vision Conference (BMVC)",
  year         = "2024",
}

Acknowledgements

Thanks to the code available at https://github.com/abhinavdhall/deepfake/tree/main/ACM_MM_2020, https://github.com/TengdaHan/DPC and https://github.com/joonson/syncnet_python.