| Name | avdeepfake1m JSON |
| Version |
0.0.0
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2024-10-12 16:14:23 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | None |
| keywords |
pytorch
ai
|
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No requirements were recorded.
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# AV-Deepfake1M
<div align="center">
<img src="assets/teaser.png">
<p></p>
</div>
<div align="center">
<a href="https://github.com/ControlNet/AV-Deepfake1M/issues">
<img src="https://img.shields.io/github/issues/ControlNet/AV-Deepfake1M?style=flat-square">
</a>
<a href="https://github.com/ControlNet/AV-Deepfake1M/network/members">
<img src="https://img.shields.io/github/forks/ControlNet/AV-Deepfake1M?style=flat-square">
</a>
<a href="https://github.com/ControlNet/AV-Deepfake1M/stargazers">
<img src="https://img.shields.io/github/stars/ControlNet/AV-Deepfake1M?style=flat-square">
</a>
<a href="https://github.com/ControlNet/AV-Deepfake1M/blob/master/LICENSE">
<img src="https://img.shields.io/badge/license-CC%20BY--NC%204.0-97ca00?style=flat-square">
</a>
<a href="https://arxiv.org/abs/2311.15308">
<img src="https://img.shields.io/badge/arXiv-2311.15308-b31b1b.svg?style=flat-square">
</a>
</div>
This is the official repository for the paper
[AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](http://arxiv.org/abs/2311.15308).
## Abstract
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.
## Dataset
### Download
We're hosting [1M-Deepfakes Detection Challenge](https://deepfakes1m.github.io/) at ACM MM 2024.
### Baseline Benchmark
| Method | AP@0.5 | AP@0.75 | AP@0.9 | AP@0.95 | 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 |
### Metadata Structure
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.
## SDK
We provide a Python library `avdeepfake1m` to load the dataset and evaluation.
### Installation
```bash
pip install avdeepfake1m
```
### Usage
Prepare the dataset as follows.
```
|- train_metadata.json
|- train_metadata
| |- ...
|- train
| |- ...
|- val_metadata.json
|- val_metadata
| |- ...
|- val
| |- ...
|- test_files.txt
|- test
```
Load the dataset.
```python
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](https://deepfakes1m.github.io/details). Then run the following code.
```python
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>"))
```
## License
The dataset is under the [EULA](eula.pdf). 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](LICENSE) for details.
## References
If you find this work useful in your research, please cite it.
```bibtex
@article{cai2023avdeepfake1m,
title = {AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset},
action = {Cai, Zhixi and Ghosh, Shreya and Adatia, Aman Pankaj and Hayat, Munawar and Dhall, Abhinav and Stefanov, Kalin},
journal = {arXiv preprint arXiv:2311.15308},
year = {2023},
}
```
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"description": "# AV-Deepfake1M\n\n<div align=\"center\">\n <img src=\"assets/teaser.png\">\n <p></p>\n</div>\n\n<div align=\"center\">\n <a href=\"https://github.com/ControlNet/AV-Deepfake1M/issues\">\n <img src=\"https://img.shields.io/github/issues/ControlNet/AV-Deepfake1M?style=flat-square\">\n </a>\n <a href=\"https://github.com/ControlNet/AV-Deepfake1M/network/members\">\n <img src=\"https://img.shields.io/github/forks/ControlNet/AV-Deepfake1M?style=flat-square\">\n </a>\n <a href=\"https://github.com/ControlNet/AV-Deepfake1M/stargazers\">\n <img src=\"https://img.shields.io/github/stars/ControlNet/AV-Deepfake1M?style=flat-square\">\n </a>\n <a href=\"https://github.com/ControlNet/AV-Deepfake1M/blob/master/LICENSE\">\n <img src=\"https://img.shields.io/badge/license-CC%20BY--NC%204.0-97ca00?style=flat-square\">\n </a>\n <a href=\"https://arxiv.org/abs/2311.15308\">\n <img src=\"https://img.shields.io/badge/arXiv-2311.15308-b31b1b.svg?style=flat-square\">\n </a>\n</div>\n\nThis is the official repository for the paper \n[AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](http://arxiv.org/abs/2311.15308).\n\n## Abstract\nThe detection and localization of highly realistic deepfake audio-visual content are challenging even for the most \nadvanced state-of-the-art methods. While most of the research efforts in this domain are focused on detecting \nhigh-quality deepfake images and videos, only a few works address the problem of the localization of small segments of \naudio-visual manipulations embedded in real videos. In this research, we emulate the process of such content generation \nand propose the AV-Deepfake1M dataset. The dataset contains content-driven (i) video manipulations, \n(ii) audio manipulations, and (iii) audio-visual manipulations for more than 2K subjects resulting in a total of more \nthan 1M videos. The paper provides a thorough description of the proposed data generation pipeline accompanied by a \nrigorous analysis of the quality of the generated data. The comprehensive benchmark of the proposed dataset utilizing \nstate-of-the-art deepfake detection and localization methods indicates a significant drop in performance compared to \nprevious datasets. The proposed dataset will play a vital role in building the next-generation deepfake localization \nmethods.\n\n## Dataset\n\n### Download\n\nWe're hosting [1M-Deepfakes Detection Challenge](https://deepfakes1m.github.io/) at ACM MM 2024.\n\n### Baseline Benchmark\n\n| Method | AP@0.5 | AP@0.75 | AP@0.9 | AP@0.95 | AR@50 | AR@20 | AR@10 | AR@5 |\n|----------------------------|--------|---------|--------|---------|-------|-------|-------|-------|\n| PyAnnote | 00.03 | 00.00 | 00.00 | 00.00 | 00.67 | 00.67 | 00.67 | 00.67 |\n| Meso4 | 09.86 | 06.05 | 02.22 | 00.59 | 38.92 | 38.81 | 36.47 | 26.91 |\n| MesoInception4 | 08.50 | 05.16 | 01.89 | 00.50 | 39.27 | 39.00 | 35.78 | 24.59 |\n| EfficientViT | 14.71 | 02.42 | 00.13 | 00.01 | 27.04 | 26.43 | 23.90 | 20.31 |\n| TriDet + VideoMAEv2 | 21.67 | 05.83 | 00.54 | 00.06 | 20.27 | 20.12 | 19.50 | 18.18 |\n| TriDet + InternVideo | 29.66 | 09.02 | 00.79 | 00.09 | 24.08 | 23.96 | 23.50 | 22.55 |\n| ActionFormer + VideoMAEv2 | 20.24 | 05.73 | 00.57 | 00.07 | 19.97 | 19.81 | 19.11 | 17.80 |\n| ActionFormer + InternVideo | 36.08 | 12.01 | 01.23 | 00.16 | 27.11 | 27.00 | 26.60 | 25.80 |\n| BA-TFD | 37.37 | 06.34 | 00.19 | 00.02 | 45.55 | 35.95 | 30.66 | 26.82 |\n| BA-TFD+ | 44.42 | 13.64 | 00.48 | 00.03 | 48.86 | 40.37 | 34.67 | 29.88 |\n| UMMAFormer | 51.64 | 28.07 | 07.65 | 01.58 | 44.07 | 43.45 | 42.09 | 40.27 |\n\n\n### Metadata Structure\n\nThe metadata is a json file for each subset (train, val), which is a list of dictionaries. The fields in the dictionary are as follows.\n- file: the path to the video file.\n- original: if the current video is fake, the path to the original video; otherwise, the original path in VoxCeleb2.\n- split: the name of the current subset.\n- 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.\n- audio_model: the audio generation model used for generating this video.\n- fake_segments: the timestamps of the fake segments. We evaluate the temporal localization performance based on this field.\n- audio_fake_segments: the timestamps of the fake segments in audio modality.\n- visual_fake_segments: the timestamps of the fake segments in visual modality.\n- video_frames: the number of frames in the video.\n- audio_frames: the number of frames in the audio.\n\n## SDK\n\nWe provide a Python library `avdeepfake1m` to load the dataset and evaluation.\n\n### Installation\n\n```bash\npip install avdeepfake1m\n```\n\n### Usage\n\nPrepare the dataset as follows.\n\n```\n|- train_metadata.json\n|- train_metadata\n| |- ...\n|- train\n| |- ...\n|- val_metadata.json\n|- val_metadata\n| |- ...\n|- val\n| |- ...\n|- test_files.txt\n|- test\n```\n\nLoad the dataset.\n\n```python\nfrom avdeepfake1m.loader import AVDeepfake1mDataModule\n\n# access to Lightning DataModule\ndm = AVDeepfake1mDataModule(\"/path/to/dataset\")\n```\n\nEvaluate the predictions. Firstly prepare the predictions as described in the [details](https://deepfakes1m.github.io/details). Then run the following code.\n\n```python\nfrom avdeepfake1m.evaluation import ap_ar_1d, auc\nprint(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]))\nprint(auc(\"<PREDICTION_TXT>\", \"<METADATA_JSON>\"))\n```\n\n## License\n\nThe dataset is under the [EULA](eula.pdf). You need to agree and sign the EULA to access the dataset.\n\nThe other parts of this project is under the CC BY-NC 4.0 license. See [LICENSE](LICENSE) for details.\n\n## References\n\nIf you find this work useful in your research, please cite it.\n\n```bibtex\n@article{cai2023avdeepfake1m,\n title = {AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset},\n action = {Cai, Zhixi and Ghosh, Shreya and Adatia, Aman Pankaj and Hayat, Munawar and Dhall, Abhinav and Stefanov, Kalin},\n journal = {arXiv preprint arXiv:2311.15308},\n year = {2023},\n}\n```\n\n",
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