Datasets:
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """HyperForensics++ dataset""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @InProceedings{hyperforensics:dataset, | |
| title={HyperForensics++: Toward Adversarial Perturbed and Object Replacement in Hyperspectral Imaging Dataset}, | |
| author={Chih-Chung Hsu, Chia-Ming Lee, Min-Tzo Ko, En-Chao Liu, Yi-Ching Cheng, Ming-Ching Chang}, | |
| year={2025} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The HyperForensics++ dataset is an advanced benchmark designed for hyperspectral image (HSI) forgery detection. | |
| It builds upon the foundational HyperForensics dataset by introducing new manipulation scenarios and enhanced techniques. | |
| """ | |
| _HOMEPAGE = "https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| _URL = "https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus/resolve/main/data.tar.gz" | |
| class HyperForensicsPlusPlus(datasets.GeneratorBasedBuilder): | |
| """HyperForensics++ dataset""" | |
| VERSION = datasets.Version("1.1.0") | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="data", | |
| version=VERSION, | |
| description="Full dataset with all the HSI in npy format",), | |
| # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), | |
| ] | |
| DEFAULT_CONFIG_NAME = "data" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "origin": datasets.Image(), # The original HSI | |
| "label": datasets.Value("string"), # The label of the image | |
| "forgery": datasets.Image(), # The HSI after forgery | |
| "method": datasets.Value("string"), # The forgery method used | |
| # The bounding box of the forgery area, in the format [x1, x2, y1, y2, z1, y2] | |
| "bbox": datasets.Sequence(feature=datasets.Value(dtype='int8'), length=6) | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| urls = _URL | |
| data_dir = dl_manager.download_and_extract(urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "metadata.jsonl"), | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "metadata.jsonl"), | |
| "split": "val", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "metadata.jsonl"), | |
| "split": "test" | |
| }, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| with open(filepath, encoding="utf-8") as f: | |
| for key, row in enumerate(f): | |
| data = json.loads(row) | |
| if self.config.name == "first_domain": | |
| # Yields examples as (key, example) tuples | |
| yield key, { | |
| "sentence": data["sentence"], | |
| "option1": data["option1"], | |
| "answer": "" if split == "test" else data["answer"], | |
| } | |
| else: | |
| yield key, { | |
| "sentence": data["sentence"], | |
| "option2": data["option2"], | |
| "second_domain_answer": "" if split == "test" else data["second_domain_answer"], | |
| } |