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AV-Deepfake1M
This is the official repository for the paper AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset.
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
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We're hosting 1M-Deepfakes Detection Challenge 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
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.
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>"))
License
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.
References
If you find this work useful in your research, please cite it.
@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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Algorithm | Hash digest | |
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SHA256 | 9992e05518474856e8b80d40dca53ea27f477509236546ced2dec35d35f9e276 |
|
MD5 | 52e388379c6ad05a7a9c59bdf22b600d |
|
BLAKE2b-256 | ccafaa80dff4b3930d213ef8c21dcc4128233d20d11ec5f304499297bf5a817f |
Hashes for avdeepfake1m-0.0.0-cp311-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3f70387e38374b5ac7011079c296d4adcc56ffc1c72128a39855c8f5817177d1 |
|
MD5 | 7401566dbdaae024364fb9afb31abb50 |
|
BLAKE2b-256 | 6b3b62a2933805b7452d73a7ece95bf7c4d974689a58a40703c49babf53042d3 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d02e45d8523af67d422cd0504c198e5358987e4c345d7887602e80a343d9e30e |
|
MD5 | 6347c93ff03d8829a9bf082de617942e |
|
BLAKE2b-256 | cd17491d43928c1d77cb49e3b6a9b15589b41ae6052ebe926aaeae99605bf66b |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-musllinux_1_2_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e00f81305b25854e5d46849db594bb188483dca07fdf4ef9cfa4bf71c9ab1ee4 |
|
MD5 | b518f6afa10e71e31c0bf02e59a459c2 |
|
BLAKE2b-256 | 57a4acf0ccf3821fc850c4d9a05632477ccac3a10f2f8d068821c832a380a195 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ef06ae9abfbec14b6a192adbafebe7fa071975317affdf9adfb5ed6efcd63657 |
|
MD5 | c293e94e10a26c0e4b367e62afb19cf4 |
|
BLAKE2b-256 | 3bf35d83f95b5ccf86e5bb7ed8c8993fe42fe883f01a4807676c3648d79aca84 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 617c9e3e8fe7407461cfc6c0b59617cdcea11ba52b23e21bee57df61f12ffce7 |
|
MD5 | 263c6716a61c7aae33745e0cba5886c8 |
|
BLAKE2b-256 | 15cb55ac569245bca55741cc505fb4975c0bf22c2eed48712ae1c0127e3a7cc2 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 56e32f7161840d5e0b070804f2f1fbcceb2afa993ff0fd50bbe1b6ce31761ca6 |
|
MD5 | ed3c11dbb130f5316629fe00bddaec7c |
|
BLAKE2b-256 | 2198fbdcf6ef923334caeb031c690448e9e7b6dbdccc0b32cbb0b9fd18260812 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9317895eaaac83da321e3b35d0b933375f6f5572456160332ac739ff95ec1b91 |
|
MD5 | b0d324580bdb442649c128c47eec25c6 |
|
BLAKE2b-256 | 210f49fe06f2cb704d3d8957f8aa7ff3c094f3f9088f4e07197719e736beb437 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | af5f428e180a18f0823aa05915db1ca1eb44417bb8cf84354c3219112c90d2e9 |
|
MD5 | 770a967dd08f8d00f8a98e0c17efcb60 |
|
BLAKE2b-256 | 85b3a192e130b316589896856b9dedaa3ba6319fb546fe796b78d39172391ff1 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2845fcbbee4c3814af0f103358e1029314ee309ff45506cc0c5f9278ebf527f7 |
|
MD5 | 190931d2e3b99dbc34fa55111bc6f8cd |
|
BLAKE2b-256 | fbfbd0bb2e52c17516a846cc8b64b6bfa8448b4d4eb93afa159610c5fa097ac2 |
Hashes for avdeepfake1m-0.0.0-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 739b784977285c534a2702f642eef51d834bf70b164d48a5fe6b2f0864270bff |
|
MD5 | a6312e96e08436260f56a2a3e6f5e50e |
|
BLAKE2b-256 | 32e86248b1d2ebe815da4e6fd945a4ea261fcbe058937709e3539634da6ddf23 |
Hashes for avdeepfake1m-0.0.0-cp310-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9c5d7f8c69b21cef12291813a13b12c5695787ab460d1ff04297cef359480c3b |
|
MD5 | 6d2a6b636042d49569a6bba22eca8e74 |
|
BLAKE2b-256 | 835d418ae44e3eaed242e2e05b579f560188e0a5ae975d00f5cd51d25804e772 |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-musllinux_1_2_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 197e95be45c2e947b542e875bbc07d065e8d255eaec5fed5c216897d6371c807 |
|
MD5 | 4f12d10a0c344aa1cb4ef1c843630c10 |
|
BLAKE2b-256 | 03e4e46993094afdda6ab4d6ff46025bd45caae1a7a4ee0787737b2f621fd607 |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-musllinux_1_2_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d3fc48bf1d73d4032d37c4eff416a23f8274065a0be72fbc292b2873ca23b3fb |
|
MD5 | e32c1cac2c567115e70c334c160c96e3 |
|
BLAKE2b-256 | e7e2a7954bcd35c9ec4504a950e7eaf0f9fec86b79c7931f6d93067ab1367f59 |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7243b3d91519bac00ff098de3fd4d627596a2dac4053cc498525457db2b037c9 |
|
MD5 | 335733c6bb2696d5a560fe91aad5d4a3 |
|
BLAKE2b-256 | 6da15c8f14233b0140b5f3ebd74735124e702dc93459e15d2fb5f988beb5902e |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 20b74e3a45d44365192c39094b1fd159bbe803654642d0690b2a0252a618b61f |
|
MD5 | 90ba65b67c50c36122cd3ceaa77d0b11 |
|
BLAKE2b-256 | 5f32767498b4040ab8e2208e7f28393b5edc824c7a5b3c1578ecee24123b11ed |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2b71510edd2cb3a92170659d21120fe48894c5d420a3034c374e6b7bbc8a8263 |
|
MD5 | 59d3e5995d0444acb223483f549f3304 |
|
BLAKE2b-256 | b53a6bab32021126333dc99dd496ac739d65ad285e22a8bca593fddd08f8f52e |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4cdd4cb8e445cfc478a936e6e0fcc03a5e2dff664f4800d334586e308274e137 |
|
MD5 | 79642c9065bac111b1e59448e07c64d5 |
|
BLAKE2b-256 | 89c60523da642d456fb2b3364394c27ce5122daf4e5cf0d4fccb8dbae6c29b67 |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 436213f8cfa339c2e06d356fc76f86014db0f683801a81cb381a89954f9ad970 |
|
MD5 | cc6ba0974e227b9a30939c44964e73b9 |
|
BLAKE2b-256 | 420c0d11cc7b5316ef1f04a5322df50c763d10e770e468dcfd3e891aa5828803 |
Hashes for avdeepfake1m-0.0.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e9857c7dec8758a0d5a4a02fb53f400ab42decd03f881935924a3f75f6d2b9ad |
|
MD5 | f2e3946c0b3ca63009b93f335db29961 |
|
BLAKE2b-256 | 474991aca4a14b9b138426c6deaa441f46e2d11ca58778a760de1360b170cd68 |
Hashes for avdeepfake1m-0.0.0-cp39-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 38a43e58fa6b46649e00508b03ba2844e1ba26da684d4909a1d9a42010ed95cf |
|
MD5 | b4056287061c78af30e52b57a486633a |
|
BLAKE2b-256 | c243e6d1dd6c0a834c9aa06c620428c30b978a6279933ab47f691b6e2fa8cd96 |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-musllinux_1_2_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d22a1dccece51dbe713db188e7995a94a51366c94044b4a71e4d946fb3901431 |
|
MD5 | 3a10f1f31e45a2d03f71574fb92ee232 |
|
BLAKE2b-256 | efe06525b2373ab32cda17fd7bb21dba3446f46a0c6ee64b1eb56ef9fb3e3e42 |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-musllinux_1_2_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | af6690f76d27aec6b570d77c9739bed0e8b158a5c11c89ed0a0a0cdfda9f371e |
|
MD5 | 5f56f30f25f07eb2b98e39b797ff65c4 |
|
BLAKE2b-256 | 3007eb86ec19e39d7623a23770454b5d2e5b7a88d3d42fa20e191309f31dad1a |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4285819cddd07c7d25225127d3ceddcb7305904780573d381dfe4a3b951c6ea7 |
|
MD5 | 70cd6cebd90abf4b84b7820a3dfbf4ce |
|
BLAKE2b-256 | d12ccd7ff9c79b8c7f3f25a261d32977b19f5b4f65cf3323c2d4c8d4dfed425a |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 01f589bc712be5753596ff2ffccc0221453081afc0eedd51303103e9e8509912 |
|
MD5 | 0882da82365574ad671c6799dce87867 |
|
BLAKE2b-256 | 165cc41275078ea0adc3b22ed3b604c695118c84a8e9b5882bf6973ff21a4009 |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6b1e86ef88206f9b128a53583cf41e1502bf6e6e3d25dcc37e87200e9d34f849 |
|
MD5 | 3bd028c3316e17d48cec078d4b01334c |
|
BLAKE2b-256 | ee349d190f1f3f56aae4760528b8fd9ee7327e6a3bb6debb4ae728e02d9018bc |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42263716308f3b07da7098f698c3fc3f63489296d85d44c45d551b0cee63404f |
|
MD5 | 3fd09925354a6bad5b6bb8a53eefe436 |
|
BLAKE2b-256 | 742ce10481bd36a7a204066b060d213963168898dc53ef1a07dda3b3f0a49089 |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a0817374d65d55e045b33e9515c4491b91f13e921dbbb399c5fabe0ac26b9efa |
|
MD5 | cb9be1ea93d59d1c8d5640d39839abfe |
|
BLAKE2b-256 | 4dd26f25d8e7776703345c1196b72e2b349f7676f97ec29d942126a4f8ebc3c3 |
Hashes for avdeepfake1m-0.0.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9aa44fcc65e678798bf7882a3b06e2349d3964a59efe87cf6800897fd65e32a7 |
|
MD5 | 4e4d1e8d01f052c0242844b7252587d0 |
|
BLAKE2b-256 | e686e2d966575659ed3f55cad74afca0b3f4d7982e0ed15bf23a58844b2c7df1 |
Hashes for avdeepfake1m-0.0.0-cp38-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e68128a349873fa80fd14ea0e46fde3f0092190bc9ab1de9803cb779f041999f |
|
MD5 | f333f2d08deb1a7624658d52a1e335b0 |
|
BLAKE2b-256 | 7c43ebd2227c94e0edbf45dc24345f81cac1d2ee1d3ac8561a5f136bc92d011c |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-musllinux_1_2_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | df3278391ea9e204b3333e12a96a117669102779c384b22e97c4cba12b4ff46e |
|
MD5 | 48d57c95c95c46f3a740d64cbca8705b |
|
BLAKE2b-256 | 093aea15fcb94d9bcc669473aeaea8c92060c5a5acc67d897cae5d878ea1ec92 |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-musllinux_1_2_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c09c2ca37e9ff1bce29351fccecde0276a24b4cb13dbde0c57e2c56c7de06fe2 |
|
MD5 | 9d8548670fdfe2fc9dff866b184df1ba |
|
BLAKE2b-256 | 0d61a79463d9bab2dc94ec4a73e5199a541ebc66e5e2bfa9b66dd1559f727714 |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d1a64526ad1a326494e39e8b8524b09b6cc11d9983033842f17f220f9c699cf5 |
|
MD5 | 67873b9ada57f5d7dc8c46368717f571 |
|
BLAKE2b-256 | 781426fc1788277d8ad530a35267ff75acf2fba9e48c720f669c09685051c54f |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 49aa5b8c0f47be17f0d7a78a7e84c1d427be52bf0c0bb340da5688aef0635092 |
|
MD5 | 18d7b3e228bb942fb57e1b68438377fc |
|
BLAKE2b-256 | 4370beeb27f8e9798257c3f8d82459791db4b3d4377478b0412ab884d9cdfba4 |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7a7ece79c8a44b34846efa7f68afa52a5abd7df4430936a247c4ddf2d8a97af0 |
|
MD5 | 3886f9d22513f95e49cffa6151a301ac |
|
BLAKE2b-256 | d948b02591b221c86c4004a4ab68dbaafd1b6ef7668a4d4017d5ec4c94f3a1e8 |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 34fcd7f37b6870b3e6b8fd7c5619dbf58984f382f4b3fb1e937682d9408c40ad |
|
MD5 | 1c8ce20ec7973b19caebc3f7cb3b9649 |
|
BLAKE2b-256 | 1ca6f89aa9681fd1b33f6ed6209218a5e1964d70ee346b935bdc85528b807f92 |
Hashes for avdeepfake1m-0.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ee1342df46e81784aa04576c0774e3586bc7504dea98863c896e423055f1d5eb |
|
MD5 | 7cbb7fc54dccb536aca11036a81423e5 |
|
BLAKE2b-256 | 712de7f7a127d06215d7d7156263b9bde09daf4b4cc2441c3cb1a46fad837991 |
Hashes for avdeepfake1m-0.0.0-cp37-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | adf0c319c1a5ef2ca5919d9b4a0317fa2f94eaac3c10c26f88a481837b9e6600 |
|
MD5 | 39dfbcd01a75dee913f8ef92da74f586 |
|
BLAKE2b-256 | f5850786be67bfe56cc1872c39f558b3075f146aad2db32a6ad8807d38f6916b |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-musllinux_1_2_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7567203bbe5671eb378e6e92e492180b9e190b5221bc8ab8cd6991782ddbac23 |
|
MD5 | 08a11e01935a9a9d8d4a70ef9f5a99ba |
|
BLAKE2b-256 | f1315ef6baed617eb705eeae4cde0e95d8779894845f78c1a17660e88b41ffa2 |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-musllinux_1_2_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f4b0fec89f023296e5d9c56ad73584619c91133a12b096dbaf9d63ed430a2cdf |
|
MD5 | 0db35752f5db55fe44a249ad84842cb5 |
|
BLAKE2b-256 | 634d5203bdd5b471a0a94d20b7bf6a58cb96cc99f12fe35d2fa60082d859337f |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3e7fb3c4e2ff7f6ac530794be5e8ac129b306f7759a4efbd87b91b1356f51338 |
|
MD5 | 5269566ecfc5adf5a0dc63b2b03a85d3 |
|
BLAKE2b-256 | 3a6f85af272467eaf14dc80464fe9d3ba8d4fc8abbaf0f0b0f44bf6d9f57fcce |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a82308fd7b3a291d00c16e12fc418e9c1757a6e32ada1bb60386332903774da3 |
|
MD5 | 58672cf9915c2a76b22516b9571cd5b8 |
|
BLAKE2b-256 | cdbb02f3ec3459ed5c39689384ffe3324bd4ede3b1485509c549da1474c8eda6 |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2d28e0b21ae11c0e132b462e0e9b58435f4c1e92d4db96f08fc9cef59b9ba87c |
|
MD5 | 52a19c6fe692ec49414b4f24f1b124a2 |
|
BLAKE2b-256 | 8714394f23daecc674b4dd3ec81aece1e0c8df2823f2bc5041e626e0b22f4cb0 |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8dbf7fba16576fc7107da70f746b7e96d259c6f5619cdc1a7f5d199e4763f47a |
|
MD5 | c828be25f80dd15fb32103c8ff8e7ef2 |
|
BLAKE2b-256 | 480070a1a4fffebdd69b92a75496f24011d587de4aac843d84d98b9262429316 |
Hashes for avdeepfake1m-0.0.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cf16d1858d97d079112547273054fd531a45c9052e75a883772da3fca62244ae |
|
MD5 | 13bd395e335bae6cf7103b4ef8833117 |
|
BLAKE2b-256 | 2b643a1b2bf0d03279d650c2b041f176a96c63eae16060242234beba7097e7e0 |