task_categories:image-to-image task_categories:video-classification language:en license:cc-by-nc-nd-4.0 region:us code
---
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
- video-classification
language:
- en
tags:
- code
dataset_info:
  features:
  - name: phone
    dtype: string
  - name: selfie
    dtype: image
  - name: video
    dtype: string
  - name: worker_id
    dtype: string
  - name: age
    dtype: int8
  - name: country
    dtype: string
  - name: gender
    dtype: string
  splits:
  - name: train
    num_bytes: 100634313
    num_examples: 30
  download_size: 568013310
  dataset_size: 100634313
---
# Anti Spoofing Real - Liveness Detection dataset
The Biometric Attack dataset consists of 98,000 videos and selfies from people from 170 countries. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. 

The dataset contains images and videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.

### The dataset includes 2 different types of files:
- **Photo** - a selfie of a person from a mobile phone, the person is depicted alone on it, the face is clearly visible.
- **Video** - filmed on the front camera, on which a person moves his/her head left, right, up and down. Duration of the video is from 10 to 20 seconds.

### Image
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0956f7614a7de991459b5bacc8e30688%2Fanti.png?generation=1685093697311106&alt=media)

The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization. 

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models. 

# 💴 For Commercial Usage: Full version of the dataset includes 50 000+ sets of files, leave a request on **[TrainingData](https://trainingdata.pro/datasets/anti-spoofing-real?utm_source=huggingface&utm_medium=cpc&utm_campaign=anti-spoofing_Real)** to buy the dataset

### Metadata for the full dataset:
- **assignment_id** - unique identifier of the media file
- **worker_id** - unique identifier of the person
- **age** - age of the person
- **true_gender** - gender of the person
- **country** - country of the person
- **ethnicity** - ethnicity of the person
- **video_extension** - video extensions in the dataset
- **video_resolution** - video resolution in the dataset
- **video_duration** - video duration in the dataset
- **video_fps** - frames per second for video in the dataset
- **photo_extension** - photo extensions in the dataset
- **photo_resolution** - photo resolution in the dataset

# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/datasets](https://trainingdata.pro/datasets/anti-spoofing-real?utm_source=huggingface&utm_medium=cpc&utm_campaign=anti-spoofing_Real)** to discuss your requirements, learn about the price and buy the dataset

# Content
### The folder **"samples"** includes 30 folders:
- corresponding to each person in the sample
- containing of selfie and video of the individual

### File with the extension .csv
includes the following information for each media file:
- **phone**: the device used to capture the media files,
- **selfie_link**: the URL to access the photo
- **video_link**: the URL to access the video
- **worker_id**: the identifier of the person who provided the media file,
- **age**: the age of the person,
- **country**: the country of origin of the person,
- **gender**: the gender of the person,
- **selfie_file_type**: the type of the photo,
- **video_file_type**: the type of the video

**[TrainingData](https://trainingdata.pro/datasets/anti-spoofing-real?utm_source=huggingface&utm_medium=cpc&utm_campaign=anti-spoofing_Real)** provides high-quality data annotation tailored to your needs.

More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>**

TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

*keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face detection, face identification, face recognition, human video dataset, video dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*