
AV-Deepfake1M
[ACM MM Award] AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
Stars: 106

The AV-Deepfake1M repository is the official repository for the paper AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset. It addresses the challenge of detecting and localizing deepfake audio-visual content by proposing a dataset containing video manipulations, audio manipulations, and audio-visual manipulations for over 2K subjects resulting in more than 1M videos. The dataset is crucial for developing next-generation deepfake localization methods.
README:
This is the official repository for the paper AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset (Best Award).
The detection and localization of highly realistic deepfake audio-visual content are challenging even for the most advanced state-of-the-art methods. While most of the research efforts in this domain are focused on detecting high-quality deepfake images and videos, only a few works address the problem of the localization of small segments of audio-visual manipulations embedded in real videos. In this research, we emulate the process of such content generation and propose the AV-Deepfake1M dataset. The dataset contains content-driven (i) video manipulations, (ii) audio manipulations, and (iii) audio-visual manipulations for more than 2K subjects resulting in a total of more than 1M videos. The paper provides a thorough description of the proposed data generation pipeline accompanied by a rigorous analysis of the quality of the generated data. The comprehensive benchmark of the proposed dataset utilizing state-of-the-art deepfake detection and localization methods indicates a significant drop in performance compared to previous datasets. The proposed dataset will play a vital role in building the next-generation deepfake localization methods.
https://github.com/user-attachments/assets/d91aee8a-0fb5-4dff-ba20-86420332fed5
We're hosting 1M-Deepfakes Detection Challenge at ACM MM 2024.
Method | [email protected] | [email protected] | [email protected] | [email protected] | AR@50 | AR@20 | AR@10 | AR@5 |
---|---|---|---|---|---|---|---|---|
PyAnnote | 00.03 | 00.00 | 00.00 | 00.00 | 00.67 | 00.67 | 00.67 | 00.67 |
Meso4 | 09.86 | 06.05 | 02.22 | 00.59 | 38.92 | 38.81 | 36.47 | 26.91 |
MesoInception4 | 08.50 | 05.16 | 01.89 | 00.50 | 39.27 | 39.00 | 35.78 | 24.59 |
EfficientViT | 14.71 | 02.42 | 00.13 | 00.01 | 27.04 | 26.43 | 23.90 | 20.31 |
TriDet + VideoMAEv2 | 21.67 | 05.83 | 00.54 | 00.06 | 20.27 | 20.12 | 19.50 | 18.18 |
TriDet + InternVideo | 29.66 | 09.02 | 00.79 | 00.09 | 24.08 | 23.96 | 23.50 | 22.55 |
ActionFormer + VideoMAEv2 | 20.24 | 05.73 | 00.57 | 00.07 | 19.97 | 19.81 | 19.11 | 17.80 |
ActionFormer + InternVideo | 36.08 | 12.01 | 01.23 | 00.16 | 27.11 | 27.00 | 26.60 | 25.80 |
BA-TFD | 37.37 | 06.34 | 00.19 | 00.02 | 45.55 | 35.95 | 30.66 | 26.82 |
BA-TFD+ | 44.42 | 13.64 | 00.48 | 00.03 | 48.86 | 40.37 | 34.67 | 29.88 |
UMMAFormer | 51.64 | 28.07 | 07.65 | 01.58 | 44.07 | 43.45 | 42.09 | 40.27 |
The metadata is a json file for each subset (train, val), which is a list of dictionaries. The fields in the dictionary are as follows.
- file: the path to the video file.
- original: if the current video is fake, the path to the original video; otherwise, the original path in VoxCeleb2.
- split: the name of the current subset.
- modify_type: the type of modifications in different modalities, which can be ["real", "visual_modified", "audio_modified", "both_modified"]. We evaluate the deepfake detection performance based on this field.
- audio_model: the audio generation model used for generating this video.
- fake_segments: the timestamps of the fake segments. We evaluate the temporal localization performance based on this field.
- audio_fake_segments: the timestamps of the fake segments in audio modality.
- visual_fake_segments: the timestamps of the fake segments in visual modality.
- video_frames: the number of frames in the video.
- audio_frames: the number of frames in the audio.
We provide a Python library avdeepfake1m
to load the dataset and evaluation.
pip install avdeepfake1m
Prepare the dataset as follows.
|- train_metadata.json
|- train_metadata
| |- ...
|- train
| |- ...
|- val_metadata.json
|- val_metadata
| |- ...
|- val
| |- ...
|- test_files.txt
|- test
Load the dataset.
from avdeepfake1m.loader import AVDeepfake1mDataModule
# access to Lightning DataModule
dm = AVDeepfake1mDataModule("/path/to/dataset")
Evaluate the predictions. Firstly prepare the predictions as described in the details. Then run the following code.
from avdeepfake1m.evaluation import ap_ar_1d, auc
print(ap_ar_1d("<PREDICTION_JSON>", "<METADATA_JSON>", "file", "fake_segments", 1, [0.5, 0.75, 0.9, 0.95], [50, 30, 20, 10, 5], [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]))
print(auc("<PREDICTION_TXT>", "<METADATA_JSON>"))
The dataset is under the EULA. You need to agree and sign the EULA to access the dataset.
The other parts of this project is under the CC BY-NC 4.0 license. See LICENSE for details.
If you find this work useful in your research, please cite it.
@inproceedings{cai2024av,
title={AV-Deepfake1M: A large-scale LLM-driven audio-visual deepfake dataset},
author={Cai, Zhixi and Ghosh, Shreya and Adatia, Aman Pankaj and Hayat, Munawar and Dhall, Abhinav and Gedeon, Tom and Stefanov, Kalin},
booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
pages={7414--7423},
year={2024},
doi={10.1145/3664647.3680795}
}
The challenge summary paper:
@inproceedings{cai20241m,
title={1M-Deepfakes Detection Challenge},
author={Cai, Zhixi and Dhall, Abhinav and Ghosh, Shreya and Hayat, Munawar and Kollias, Dimitrios and Stefanov, Kalin and Tariq, Usman},
booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
pages={11355--11359},
year={2024},
doi={10.1145/3664647.3689145}
}
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