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# 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 numpy as np
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 __init__(self, **kwargs):
# You can add any custom arguments here that you want to pass to the builder
# They will be passed to the constructor of the parent class
super(HyperForensicsPlusPlus, self).__init__(writer_batch_size=4, **kwargs)
def _info(self):
features = datasets.Features(
{
"origin": datasets.Array3D(dtype="int16", shape=(256, 256, 172)), # The original HSI
"label": datasets.Value("string"), # The label of the image
"forgery": datasets.Array3D(dtype="int16", shape=(256, 256, 172)), # 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='int16'), 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": data_dir,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "validation",
},
),
#datasets.SplitGenerator(
# name=datasets.Split.TEST,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": data_dir,
# "split": "testing"
# },
#),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
filepath = os.path.join(filepath, "data_testing")
with open(os.path.join(filepath, "metadata.jsonl"), encoding="utf-8") as f:
metadata = json.load(f) # Load the nested JSON object (train, validation, testing)
# Select the appropriate split (train, validation, or testing)
records = metadata[split]
for key, record in enumerate(records):
file_prefix = record["file_prefix"]
label = record["label"]
bbox = record["bbox"]
# Construct paths for the origin and forgery files
origin_path = os.path.join(
filepath, "ADMM-ADAM", "config0", f"{file_prefix}_inpaint_result(0).npy"
)
forgery_path = os.path.join(
filepath, "ADMM-ADAM", "config0", f"{file_prefix}_inpaint_result(0).npy"
)
# Load the .npy files as images
origin_image = np.load(origin_path) #np.load(origin_path)
forgery_image = np.load(forgery_path) #np.load(forgery_path)
# Yield the example
yield key, {
"origin": origin_image,
"label": label,
"forgery": forgery_image,
"method": "ADMM-ADAM", # Hardcoded for now; can be dynamic if needed
"bbox": bbox,
} |