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Commercial
2D Printed Photos Attacks and Replay Attacks Images Dataset
Large-scale 2D printed mask dataset with 4.1M+ images from 80,000+ individuals, designed for face anti-spoofing, liveness detection, and face recognition. It includes diverse printed attacks, replay attacks, face presentation attacks, and printed photos, with bounding box annotations, JSON labels, and metadata (age, gender, ethnicity) to support training data for anti-spoofing methods and detection algorithms.
The dataset covers multiple attack types, including screen replays, paper photos, color printouts, masks, tablets, and annotated mask samples, providing high-quality JPG images.
-
- images
- 4,1M+
-
- people
- 80,000+
- Computer Vision
- Machine learning
- Security
- Anti-spoofing
- Liveness Detection
- Computer Vision
- Machine learning
- Security
- Anti-spoofing
- Liveness Detection
Large-scale 2D printed mask dataset with 4.1M+ images from 80,000+ individuals, designed for face anti-spoofing, liveness detection, and face recognition. It includes diverse printed attacks, replay attacks, face presentation attacks, and printed photos, with bounding box annotations, JSON labels, and metadata (age, gender, ethnicity) to support training data for anti-spoofing methods and detection algorithms.
The dataset covers multiple attack types, including screen replays, paper photos, color printouts, masks, tablets, and annotated mask samples, providing high-quality JPG images.
- Computer Vision
- Machine learning
- Security
- Anti-spoofing
- Liveness Detection
-
- images
- 4,1M+
-
- people
- 80,000+
Dataset Info
| Characteristic | Data |
| Description | Images of individuals with printed masks and replay attack |
| Data types | Image |
| Tasks | Anti-spoofing, Liveness Detection, Face Recognition |
| Number of image | 4,1M+ |
| Number of people | 80,000+ |
| Name of categories | archive(Faces displayed on screens), color(Color printouts of faces), easy(Masked faces on screens), frame, mask, print, faces on screen, all(Color printouts (generic)), bald, tablet, fake, Moscow newspapers, paper, mask annotated, paper photo |
| Labeling | Metadata (age, gender, ethnicity) |
| Gender | Male, Female |
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Technical
Characteristics
| Characteristic | Data |
| Image extension | JPG |
| Extension of labeling file | Bbox, json |
Dataset Use Cases
FAQs
What annotations are included?
The dataset provides bounding box annotations, JSON labels, and demographic metadata including age, gender, and ethnicity. These annotations support attack detection, face recognition, detection algorithms, and machine learning workflows for biometric security.
Which attack types are represented in the dataset?
The dataset covers a wide range of presentation attacks, including screen replay attacks, printed photos, paper photos, color printouts, tablet displays, printed masks, fake faces, and annotated mask samples. This diversity helps AI models become more robust against multiple real-world spoofing techniques.
How was the dataset collected?
The dataset was collected by a Unidata partner specifically for AI development and biometric security applications. It contains high-quality images representing numerous printed attacks and replay attack scenarios suitable for training anti-spoofing methods.
Is this a real-world dataset or synthetic data?
This is a real-world image dataset, not synthetic data. It contains authentic images collected for biometric AI training and includes genuine examples of printed photos, screen replay attacks, and other presentation attack scenarios.
Can I download a sample of the dataset before purchasing?
Yes. Unidata provides a free sample of the Replay Attack Dataset so you can evaluate the image quality, annotation structure, metadata, and attack categories before purchasing the complete dataset. This allows you to verify compatibility with your anti-spoofing models and training workflow.
Can I request a custom anti-spoofing dataset?
Yes. Unidata can create custom anti-spoofing replay attack datasets tailored to your project requirements. We can customize attack types, demographic distribution, annotation formats, metadata, image specifications, and dataset size for your biometric security application.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for evaluation and testing, while complete datasets are available exclusively through purchase. This allows organizations to validate the data before making a licensing decision.
Do Unidata datasets comply with GDPR and other privacy regulations?
Yes. All Unidata datasets are curated in compliance with GDPR and applicable data protection regulations. Data is collected from legally permissible sources to support ethical AI development and responsible biometric research.
How are Unidata datasets stored?
All datasets are securely stored on AWS cloud infrastructure with high availability and scalability. Storage and management practices follow ISO 27001 and ISO 27701 standards to ensure strong information security and privacy protection.
Still have questions about using Unidata datasets?
Read our user-guides
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