Anti Spoofing Real Dataset - 98,000 files

# 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

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=kaggle&utm_medium=cpc&utm_campaign=anti-spoofing-live)** 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=kaggle&utm_medium=cpc&utm_campaign=anti-spoofing-live) 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=kaggle&utm_medium=cpc&utm_campaign=anti-spoofing-live)** provides high-quality data annotation tailored to your needs.

*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*

### 防伪活体检测数据集
生物识别攻击数据集包含来自170个国家的98,000个视频和自拍。这些视频通过捕捉真实个体呈现的伪装面部表情而收集。本数据集提出了一种新颖的方法，该方法学习并检测伪装技术，从真实面部图像中提取特征，以防止虚假用户捕获此类信息。

数据集包含不同分辨率、视角和颜色的真实人类图像和视频，使其成为研究反伪装技术的全面资源。

### 数据集包含两种不同类型的文件：
- **照片** - 使用手机自拍的照片，照片上仅显示一个人的面部，面部清晰可见。
- **视频** - 使用前置摄像头拍摄，其中一个人左右、上下移动头部。视频时长为10至20秒。

### 图像

本数据集提供了结合和应用不同技术、方法和模型以解决区分真实和伪装输入的挑战性任务的数据，为在主动认证系统中提供有效的反伪装解决方案提供了支持。随着新型设备，如手机，由于可创建重放、反射和深度等技术的可用性而变得容易受到伪装攻击，这些解决方案至关重要。

我们的数据集还探讨了使用神经网络架构，如深度神经网络，以促进识别面部不同区域的区分性模式和纹理，从而提高反伪装模型的准确性和泛化能力。

# 💴 商业用途：完整版数据集包含50,000+组文件，请在**[TrainingData](https://trainingdata.pro/datasets/anti-spoofing-real?utm_source=kaggle&utm_medium=cpc&utm_campaign=anti-spoofing-live)**上留下请求以购买数据集

### 完整数据集的元数据：
- **assignment_id** - 媒体文件的唯一标识符
- **worker_id** - 人员的唯一标识符
- **age** - 人员的年龄
- **true_gender** - 人员的性别
- **country** - 人员的国籍
- **ethnicity** - 人员的种族
- **video_extension** - 数据集中的视频扩展名
- **video_resolution** - 数据集中的视频分辨率
- **video_duration** - 数据集中的视频时长
- **video_fps** - 数据集中视频的帧率
- **photo_extension** - 数据集中的照片扩展名
- **photo_resolution** - 数据集中的照片分辨率

# 💴 购买数据集：此仅为数据示例。请在**[https://trainingdata.pro/datasets](https://trainingdata.pro/datasets/anti-spoofing-real?utm_source=kaggle&utm_medium=cpc&utm_campaign=anti-spoofing-live)**上留下请求，讨论您的需求、了解价格并购买数据集

# 内容
### “samples”文件夹包含30个文件夹：
- 对应每个样本中的人员
- 包含个人的自拍和视频

### 扩展名为.csv的文件
包含以下信息，针对每个媒体文件：
- **phone** - 捕捉媒体文件所用的设备
- **selfie_link** - 访问照片的URL
- **video_link** - 访问视频的URL
- **worker_id** - 提供媒体文件的人员的标识符
- **age** - 人员的年龄
- **country** - 人员的国籍起源
- **gender** - 人员的性别
- **selfie_file_type** - 照片的类型
- **video_file_type** - 视频的类型

**[TrainingData](https://trainingdata.pro/datasets/anti-spoofing-real?utm_source=kaggle&utm_medium=cpc&utm_campaign=anti-spoofing-live)**提供符合您需求的高质量数据标注。

*关键词：ibeta level 1, ibeta level 2, 活体检测系统，活体检测数据集，生物识别数据集，生物识别数据集，生物识别系统攻击，反伪装数据集，面部活体检测，深度学习数据集，面部伪装数据库，面部反伪装，面部检测，面部识别，面部识别，人类视频数据集，视频数据集，手机攻击数据集，面部反伪装，大规模面部反伪装，丰富标注反伪装数据集*