Display Replay Dataset for Liveness Detection
There are >9K screen replay attacks using variety of devices
Check samples on Kaggle
Introduction
The Display Replay Attack Dataset is a comprehensive resource designed to improve anti-spoofing technology by identifying replay attacks across PC displays and mobile devices. This dataset combines authentic selfies from over 6,500 participants with 9,000+ replay attacks performed across computer monitors, laptops, and mobile phones. By offering diverse lighting conditions, devices, and display scenarios on both PC and mobile platforms, this dataset supports the development of robust liveness detection models for multi-platform biometric security
PC Display Replay Attacks
5,000+ video replay attacks recorded on computer monitors and laptops
Key Characteristics:
- Participants: 4,000+ diverse individuals (balanced gender and ethnicity representation)
- Video Duration: Minimum 12 seconds per attack
- Camera Movement: Dynamic angles with varied positioning
- Devices: Multiple monitor brands and laptop screens
- Conditions: Diverse lighting and environmental scenarios
- Quality: High-quality selfies (720p or greater, no filters)
- Certification: iBeta Level 1 standards
Authentic selfies were collected through voluntary contributions from 1,000+ participants. Replay attacks were then performed by displaying these selfies on various computer monitors and laptop screens, recorded from multiple dynamic angles to simulate realistic attack scenarios
5,000+ video replay attacks recorded on computer monitors and laptops
Key Characteristics:
- Participants: 1,000+ diverse individuals (balanced gender and ethnicity representation)
- Video Duration: Minimum 12 seconds per attack
- Camera Movement: Dynamic angles with varied positioning
- Devices: Multiple monitor brands and laptop screens
- Conditions: Diverse lighting and environmental scenarios
- Quality: High-quality selfies (720p or greater, no filters)
- Certification: iBeta Level 1 standards
Authentic selfies were collected through voluntary contributions from 1,000+ participants. Replay attacks were then performed by displaying these selfies on various computer monitors and laptop screens, recorded from multiple dynamic angles to simulate realistic attack scenarios
Mobile Display Replay Attacks
4,000+ video replay attacks captured across smartphones
Key Characteristics:
- Participants: 2,300 diverse individuals (balanced gender and ethnicity representation)
- Video Duration: ~5 seconds per attack with zoom sequences
- Camera Movement: Zoom-in and zoom-out effects throughout
- Devices: 15 different mobile devices (low, medium, and high-end smartphones)
- Realism: No visible phone borders to mimic authentic attacks
- Conditions: Various screen types, qualities, and lighting environments
- Quality: High-quality selfies (720p or greater, no filters)
- Certification: iBeta Level 1 standards
Authentic selfies were collected through voluntary contributions from 1,500 participants. Replay attacks were performed by displaying these selfies on 15 different mobile devices spanning various price ranges and screen qualities, captured with dynamic zoom effects to simulate realistic mobile-based attack behaviors
3,000+ video replay attacks captured across smartphones
Key Characteristics:
- Participants: 1,500 diverse individuals (balanced gender and ethnicity representation)
- Video Duration: ~5 seconds per attack with zoom sequences
- Camera Movement: Zoom-in and zoom-out effects throughout
- Devices: 15 different mobile devices (low, medium, and high-end smartphones)
- Realism: No visible phone borders to mimic authentic attacks
- Conditions: Various screen types, qualities, and lighting environments
- Quality: High-quality selfies (720p or greater, no filters)
- Certification: iBeta Level 1 standards
Authentic selfies were collected through voluntary contributions from 1,500 participants. Replay attacks were performed by displaying these selfies on 15 different mobile devices spanning various price ranges and screen qualities, captured with dynamic zoom effects to simulate realistic mobile-based attack behaviors
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
Technology company from Vietnam: iBeta Level 2 success
A Vietnam-based AI/Big Data firm coached by Axon Labs passed iBeta PAD Level 2 on the first try with 0% successful spoofs; the solution claims 99.9% face-recognition accuracy
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
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
The price depends on your specific requirements. Please submit a request to receive a free consultation
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Didn't find what you were looking for?
Our collection includes many datasets for various requests
iBeta Level 1 Dataset
– 35,000+ videos
– 85+ participants
– zoom in and
zoom out
iBeta Level 2 Dataset
– 25 000+ videos
– 3D masks
– iBeta Level 2
Display Replay Dataset for Liveness Detection
– 9,000+ videos
– 6,500+ participants
– Balanced mix of genders and ethnicities
Photo Print Dataset
– 7000+ videos.
– 10-20 second each video
– Mix of genders
