Omkar2118/datasets huggingface dataset:
NNID
Full name: Nearly Nested Image Datasets

We build what we name the Nearly-Nested Image Datasets (NNID) such that each dataset owns images of the same dimension, and each dataset is issued from a cropped version of the images belonging to the dataset with the biggest dimensions. This last dataset is named mother dataset and the images are named mother images.  

By using NNID we ensure that the development is the same in all of the datasets. We also impose, as an additional constraint, that the difficulty of each dataset is the same. By the same difficulty, we mean that the distribution of costs is the same whatever the dataset. This additional constraint implies a specific way to crop the images, and most importantly, ensure that the experimental results obtained between the various dimension will be comparable since the source cost distribution of each dataset is the same. With the NNID we are able to avoid any impact of the development or the difficulty, on the experimental results; All the datasets are very similar except for the dimension.

We use, as a mother dataset, the LSSD dataset. LSSD is a mix of RAW images from ALASKA#2, BOSS, StegoApp DB, Wesaturate, RAISE, and Dresden datasets and uses a modified development script issued from the Alaska competition.

Given the NNID and an embedding algorithm, we focus on the average accuracy obtained by a classifier for each dataset. In order to obtain the relative payload size to embed for each dimension, we go by a dichotomous method, by running, for each dimension, multiple detections until finding the desired accuracy.
ArtiFact
Full name: Artificial and Factual Image Dataset for Synthetic Image Detection

The ArtiFact dataset is a large-scale image dataset that aims to include a diverse collection of real and synthetic images from multiple categories, including Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and many other real-life objects. The dataset comprises 8 sources that were carefully chosen to ensure diversity and includes images synthesized from 25 distinct methods, including 13 GANs, 7 Diffusion, and 5 other miscellaneous generators. The dataset contains 2,496,738 images, comprising 964,989 real images and 1,531,749 fake images.

To ensure diversity across different sources, the real images of the dataset are randomly sampled from source datasets containing numerous categories, whereas synthetic images are generated within the same categories as the real images. Captions and image masks from the COCO dataset are utilized to generate images for text2image and inpainting generators, while normally distributed noise with different random seeds is used for noise2image generators. The dataset is further processed to reflect real-world scenarios by applying random cropping, downscaling, and JPEG compression, in accordance with the [IEEE VIP Cup 2022 standards](https://grip-unina.github.io/vipcup2022/).

The ArtiFact dataset is intended to serve as a benchmark for evaluating the performance of synthetic image detectors under real-world conditions. It includes a broad spectrum of diversity in terms of generators used and syntheticity, providing a challenging dataset for image detection tasks.


* Total number of images: 2,496,738
* Number of real images: 964,989
* Number of fake images: 1,531,749
* Number of generators used for fake images: 25 (including 13 GANs, 7 Diffusion, and 5 miscellaneous generators)
* Number of sources used for real images: 8
* Categories included in the dataset: Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and other real-life objects
* Image Resolution: 200 x 200

Introducing paper: ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
VoiceBank+DEMAND

VoiceBank+DEMAND  is a noisy speech database for training speech enhancement algorithms and TTS models.  The database was designed to train and test speech enhancement methods that operate at 48kHz. A more detailed description can be found in the paper associated with the database. Some of the noises were obtained from the Demand database, available here: http://parole.loria.fr/DEMAND/ . The speech database was obtained from the Voice Banking Corpus, available here: http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz .
V-PCCD
Full name: simulated Visual Point Cloud Change Detection dataset

A simulated dataset built in Unreal Engine 4 with AirSim. Designed for visual point cloud change detection. Including GT point clouds before changes and after changes. Besides, 4 trajectories with stereo camera and IMU data are recorded for change detection task.

Introducing paper: Point Cloud Change Detection With Stereo V-SLAM:Dataset, Metrics and Baseline