---
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
language:
- en
tags:
- ibeta
- replay attack
- video
- liveness detection
- biometric
- anti-spoofing
dataset_info:
  features:
  - name: video_file
    dtype: string
  - name: assignment_id
    dtype: string
  - name: worker_id
    dtype: string
  - name: gender
    dtype: string
  - name: age
    dtype: uint8
  - name: country
    dtype: string
  - name: resolution
    dtype: string
  splits:
  - name: train
    num_bytes: 1547
    num_examples: 10
  download_size: 623356178
  dataset_size: 1547
size_categories:
- 10K<n<100K
---
# High Resolution Live Attacks - Biometric Attack dataset
The anti spoofing dataset includes live-recorded Anti-Spoofing videos from around the world, captured via **high-quality** webcams with Full HD resolution and above. 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 **views, and colors**, making it a comprehensive resource for researchers working on anti-spoofing technologies.

# The dataset is created on the basis of [iBeta Level 1 Dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=high_quality_webcam_video_attacks)

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1ffb68e96724140488b944b22c68580c%2F(1).png?generation=1684702390091084&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. 

# Webcam Resolution
The collection of different video resolutions from Full HD (1080p) up to 4K (2160p) is provided, including several intermediate resolutions like QHD (1440p)

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fc07c45d6c6558291a2923d24eeb43d1b%2FResoluo-de-tela-sem-imagem.webp?generation=1684703424049108&alt=media)

# Metadata

Each attack instance is accompanied by the following details:

- Unique attack identifier
- Identifier of the user recording the attack
- User's age
- User's gender
- User's country of origin
- Attack resolution

Additionally, the model of the webcam is also specified.

Metadata is represented in the `file_info.csv`.

##  👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 25,000+ human images & videos - [Full dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=high_quality_webcam_video_attacks)

#**🚀 You can learn more about our high-quality unique datasets [here](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=high_quality_webcam_video_attacks)**

*keywords: 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, ibeta dataset, human video dataset, video dataset, high quality video dataset, hd video dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*