Display Attacks Dataset
There are >5K Screen replay attacks using variety of devices
Check samples on Kaggle
Dataset details
The dataset includes selfies from over 1,000 people. Later, a team of over 200 people made 5,000+ replay display attacks based on these selfies. The attacks provide diversity of lighting, devices, and screens.
Introduction
The Liveness Detection: Display Replay Attacks Dataset is a comprehensive resource designed to improve anti-spoofing technology, specifically in identifying display replay attacks. This dataset features authentic selfies from over 1,000 participants, followed by 5,000+ replay attacks performed by a team of over 200 people. By offering a variety of lighting, devices, and display scenarios, this dataset supports the development of robust liveness detection models
Dataset summary
With a balanced representation of gender and ethnicity, the dataset provides over 5,000 replay attack scenarios based on authentic selfies from diverse participants. Each attack involves dynamic camera angles, enabling models to accurately distinguish between live and replayed display images in real-world conditions
Source and collection methodology
The dataset was collected through voluntary contributions of selfies from 1,000+ individuals, each image of high quality (720p or greater) and free of filters. Replay display attacks were then performed on various screens and recorded from multiple angles, with each video lasting at least 12 seconds to simulate realistic attack scenarios
Use cases and applications
Ideal for developers and researchers focusing on liveness detection, this dataset is particularly useful for training models to identify display replay attacks. Its applications include enhancing facial recognition systems and biometric authentication, as well as improving general anti-spoofing measures within security solutions
How industry leaders achieve superior liveness detection with our dataset
Fintech Company from Brazil: iBeta Level 1 Success
One of the largest fintechs in Brazil approached us to prepare an active biometric authentication system for iBeta Level 1 certification
Digital Bank from Vietnam: iBeta Level 2 success
Digital bank from Vietnam asked Axon Labs to prepare its anti-spoofing model in order to pass iBeta Level 2 on the first attempt, and the goal was achieved
Financial institution in LatAm: iBeta Level 1 & 2 Success
A leading financial infrastructure player in Latin America successfully obtained iBeta Level 1 and Level 2 certifications, relying on data provided by Axon Labs
Dataset features
- Over 1,000 individuals shared selfies
- Balanced mix of genders and ethnicities
- More than 5,000 display attacks crafted from these selfies
Real life selfies description
- Each person provided one selfie
- Selfies are at least 720p quality
- Faces are clear with no filters
Replay display attacks description
- Videos last at least 12 seconds
- Cameras move slowly, showing attacks from various angles
Other Datasets for iBeta 1:
Download information
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
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We collect data from our internal team. All information is further verified by our specialists
Once your enquiry has been sent, we will contact you to discuss the details and complete the necessary paperwork. The timing of receiving the dataset depends on the specific request and additional requirements
Our unique selling point is to provide legally clean datasets to our customers. We obtain the consent from all the participants to use their data for AI model development. We are able to provide comprensive reporting on the licensing, data collection and privacy compliance of our datasets. Although there seems to be a diverse response to how to control AI development and deployment, we are able to service global customers seeking to launch global AI products.
The dataset follows iBeta testing protocols and includes diverse attack scenarios that mirror real-world spoofing attempts. It covers both passive and active liveness testing requirements with proper demographic representation and standardized capture conditions essential for certification preparation
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Our collection includes many datasets for various requests
iBeta Level 1 Paper
– 22,000+ videos
– 80+ participants
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Replay Display attacks
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– 1,000+ participants
– Balanced mix of genders and ethnicities
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Silicone Mask Dataset
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Liveness Detection
