AxonData/2D_print_attack_dataset

---
license: cc-by-4.0
task_categories:
- image-classification
- image-feature-extraction
- video-classification
- image-segmentation
language:
- en
tags:
- biology
size_categories:
- 1K<n<10K
---
## Photo Print Attacks Dataset: 1K Individuals

Face Anti-Spoofing Liveness dataset videos with Zoom in effect, High-Res Print

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## Photo Print attack dataset (1K individuals+) for Presentation Attack Detection level 1 (PAD)

This dataset focuses on photo print attacks which is used by both iBeta and NIST FATE to assess liveness detection algorithms. This dataset is tailored for training AI models to identify photo print attacks on individuals. Print photo attacks include Zoom effects as mandated by NIST FATE for improved AI training.

## Dataset Description:

- 1,000+ Participants: Engaged in the project
- Diverse Representation: Balanced mix of genders and ethnicities
- 1,000+ Photo Print Attacks: Executed on the participants

## Photo Print attack description:

- Each attack comprises of 15-20 sec. video with Zoom in effects
- High-quality photos with realistic colors
- No visible image borders during the Zoom-in phase
- Paper attacks conducted on flat photos with a straight view on the camera (not bent or skewed)

## Potential Use Cases:
Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and photo print attacks with high accuracy

Keywords:
Print photo attack dataset, Antispoofing for AI, Liveness Detection dataset for AI, Spoof Detection dataset, Facial Recognition dataset, Biometric Authentication dataset, AI Dataset, PAD Attack Dataset, Anti-Spoofing Technology, Facial Biometrics, Machine Learning Dataset, Deep Learning

原始信息汇总

数据集概述

数据集名称

Photo Print Attacks Dataset: 1K Individuals

数据集类型

• 图像分类
• 图像特征提取
• 视频分类
• 图像分割

语言

• 英语

标签

• 生物学

数据规模

• 1K<n<10K

数据集描述

• 参与者数量：1,000+
• 多样性：性别和种族平衡
• 攻击类型：1,000+ 照片打印攻击

攻击描述

• 视频时长：每段攻击视频包含15-20秒的缩放效果
• 照片质量：高分辨率，真实色彩
• 边界：缩放过程中无可见图像边界
• 攻击方式：平面照片，直接对准摄像头（无弯曲或倾斜）

潜在应用场景

• 活体检测：适用于训练和评估活体检测模型，区分自拍照和照片打印攻击

关键词

• 打印照片攻击数据集
• AI防欺骗
• AI活体检测数据集
• 欺骗检测数据集
• 面部识别数据集
• 生物识别认证数据集
• AI数据集
• PAD攻击数据集
• 防欺骗技术
• 面部生物识别
• 机器学习数据集
• 深度学习