# 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 tifffile as tiff # Install with `pip install tifffile` 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" # }, #), ] def _load_npy_as_image(self, npy_path): """ Load a .npy file and convert it to a PIL Image for datasets.Image(). Not using in current scope. """ array = np.load(npy_path) # Load the .npy file as a NumPy array image = tiff.imread(npy_path) # Convert to a PIL Image return image # 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, }