Display Replay Dataset for Liveness Detection
There are >9K screen replay attacks using variety of devices
Check samples on Kaggle
Dataset Summary
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Parameter
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Value
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Volume
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PC display replay attacks: 5,000+ videos
Mobile display replay attacks: 4,000+ videos Total participants: 6,500+ across both components |
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Coverage
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Display replay attacks across PC monitors, laptops, and mobile screens
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Demographics
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Adults aged 18–65, balanced gender, multi-ethnic
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Devices
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iOS and Android phones; PC monitors and laptops
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Conditions
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Indoor and outdoor, varied lighting, multiple display brightness levels
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Introduction
The Display Replay Attack Dataset is a comprehensive training resource designed to improve anti-spoofing technology against display replay attacks across PC monitors, laptops, and mobile devices. It combines authentic selfies from over 6,500 participants with 9,000+ replay attacks performed across two distinct sub-collections: PC display replays (5,000+ videos) and mobile display replays (4,000+ videos). With diverse lighting conditions, devices, and display scenarios on both PC and mobile platforms, this dataset supports the development of robust liveness detection models for iBeta Level 1 certification, which tests biometric systems against ISO/IEC 30107-3 – the international standard for biometric presentation attack detection
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
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
Use cases and applications
- Display Replay Detection: Train liveness detection models specifically against PC and mobile screen replay attacks – the most common 2D presentation attack vector encountered in production verification flows
- Multi-Platform Anti-Spoofing: Build models robust to replay attacks across heterogeneous attack surfaces, from desktop monitors to smartphone displays, in a single training set
- iBeta Level 1 Certification Preparation: Test your liveness system against realistic display replay attack vectors before submitting to iBeta Level 1 certification
iBeta Success Stories
Datasets like this contributed to 21% of companies that passed iBeta certification in 2025 – all Axon Labs clients
From Failed Attempt to Success: UK Fintech Passed iBeta 2
UK fintech invested in datasets from another vendor for iBeta 2 and failed. The gap analysis revealed critical missing attack vectors. They needed a solution fast
Double Success: E-KYC Startup Achieves iBeta 1 & 2
A US-based e-KYC startup needed to prove their facial recognition technology met the highest industry security standards. Their ambitious goal: pass iBeta 1 and 2 Certification
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
Other Datasets for iBeta 1:
Legal & Compliance
We prioritize data privacy, ethical AI development, and regulatory compliance. Our Replay Attack Dataset is collected and processed in full accordance with global data protection standards including GDPR, ensuring legality, security, and responsible AI practices
Download information
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
Have a question?
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
iBeta Level 3 Dataset
– 10,000+ videos
– 12 Unique Masks
– iBeta Level 3
Display Replay Dataset for Liveness Detection
– 9,000+ videos
– 6,500+ participants
– Balanced mix of genders and ethnicities
