PolyGlotFake is a multilingual and multimodal deepfake dataset meticulously designed to address the challenges and demands of deepfake detection technologies. It consists of videos with manipulated audio and visual components across seven languages, employing advanced Text-to-Speech, voice cloning, and lip-sync technologies.
Please download from this link: https://drive.google.com/file/d/1aBWLii-TbrpKNLSTwpmjqu98eKovWLxF/view?usp=drive_link
| DataSet | Release Data | Manipulated Modality | Multilingual | Real video | Fake video | Total video | Manipulation Methods | Techniques Labeling | Attribute Labeling |
|---|---|---|---|---|---|---|---|---|---|
| UADFV | 2018 | V | No | 49 | 49 | 98 | 1 | No | No |
| TIMI | 2018 | V | No | 320 | 640 | 960 | 2 | No | No |
| FF++ | 2019 | V | No | 1,000 | 4,000 | 5,000 | 4 | No | No |
| DFD | 2019 | V | No | 360 | 3,068 | 3,431 | 5 | No | No |
| DFDC | 2020 | A/V | No | 23,654 | 104,500 | 128,154 | 8 | No | No |
| DeeperForensics | 2020 | V | No | 50,000 | 10,000 | 60,000 | 1 | No | No |
| Celeb-DF | 2020 | V | No | 590 | 5,639 | 6,229 | 1 | No | No |
| FFIW | 2020 | V | No | 10,000 | 10,000 | 20,000 | 1 | No | No |
| KoDF | 2021 | V | No | 62,166 | 175,776 | 237,942 | 5 | No | No |
| FakeAVCeleb | 2021 | A/V | No | 500 | 19,500 | 20,000 | 4 | No | Yes |
| DF-Platter | 2023 | V | No | 133,260 | 132,496 | 265,756 | 3 | No | Yes |
| PolyGlotFake | 2023 | A/V | Yes | 766 | 14,472 | 15,238 | 10 | Yes | Yes |
- Total Videos: 15,238
- Real Videos: 766
- Fake Videos: 14,472
- Resolution: 1280x720
- Average Video Duration: 11.79 seconds
- Language: English; French; Spanish; Russian; Chinese; Arabic; Japanese
- Synthesis methods: Audio manipulation: Bark+FreeVC; MicroTTS+FreeVC; XTTS; Tacotron+FreeVC; Vall-E-X Video manipulation: VideoRetalking; Wav2Lip
| Type | Detector | Backbone | FakeAVCeleb | DFDC | PolyGlotFake |
|---|---|---|---|---|---|
| Naive | MesoNet | Designed | 0.7332 | 0.5906 | 0.5672 |
| Naive | MesoInception | Designed | 0.7945 | 0.6344 | 0.5831 |
| Naive | Xception | Xception | 0.9169 | 0.6530 | 0.6052 |
| Naive | EfficienNet-B4 | EfficienNet | 0.9023 | 0.6020 | 0.5769 |
| Spatial | Capsule | Capsule | 0.8663 | 0.6146 | 0.6068 |
| Spatial | FFD | Xception | 0.9285 | 0.6583 | 0.5960 |
| Spatial | CORE | Xception | 0.9345 | 0.6625 | 0.6220 |
| Spatial | RECCE | Designed | 0.9396 | 0.6884 | 0.6596 |
| Spatial | DSP-FWA | Xception | 0.9115 | 0.6929 | 0.6658 |
| Frequency | F3Net | Xception | 0.9416 | 0.6452 | 0.6439 |
| Frequency | SRM | Xception | 0.9043 | 0.6346 | 0.6143 |
| Ensemble | XRes | Designed | 0.9556 | 0.7042 | 0.6835 |
Access to the dataset is restricted to academic institutions and is intended solely for research use. It complies with fair use principles through its transformative, non-commercial use, by including only brief excerpts (approximately 20 seconds) from each video source, and ensuring that these excerpts do not adversely affect the copyright owners' ability to earn revenue from their original content. Should any copyright owner feel their rights have been infringed, we are committed to promptly removing the contested material from our dataset.
@misc{hou2024polyglotfake,
title={PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset},
author={Yang Hou and Haitao Fu and Chunkai Chen and Zida Li and Haoyu Zhang and Jianjun Zhao},
year={2024},
eprint={2405.08838},
archivePrefix={arXiv},
primaryClass={cs.SD}
}