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import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {printed_photos_attacks},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The dataset consists of 40,000 videos and selfies with unique people. 15,000
attack replays from 4,000 unique devices. 10,000 attacks with A4 printouts and
10,000 attacks with cut-out printouts.
"""
_NAME = 'printed_photos_attacks'
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = "cc-by-nc-nd-4.0"
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class PrintedPhotosAttacks(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(description=_DESCRIPTION,
features=datasets.Features({
'attack': datasets.Value('string'),
'live_selfie': datasets.Image(),
'live_video': datasets.Value('string')
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE)
def _split_generators(self, dl_manager):
attacks = dl_manager.download(f"{_DATA}attack.tar.gz")
live_selfies = dl_manager.download(f"{_DATA}live_selfie.tar.gz")
live_videos = dl_manager.download(f"{_DATA}live_video.tar.gz")
attacks = dl_manager.iter_archive(attacks)
live_selfies = dl_manager.iter_archive(live_selfies)
live_videos = dl_manager.iter_archive(live_videos)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={
'attacks': attacks,
"live_selfies": live_selfies,
'live_videos': live_videos
}),
]
def _generate_examples(self, attacks, live_selfies, live_videos):
for idx, ((attack_path, attack), (live_selfie_path, live_selfie),
(live_video_path, live_video)) in enumerate(
zip(attacks, live_selfies, live_videos)):
yield idx, {
'attack': attack_path,
'live_selfie': {
'path': live_selfie_path,
'bytes': live_selfie.read()
},
'live_video': live_video_path
}
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