iBeta Level 1 Dataset
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Introduction
The iBeta Level 1 Paper & Replay Attacks Dataset offers a comprehensive collection of presentation attacks (PAD) tailored for iBeta Level 1 testing. Beyond paper-based masks and printouts, it includes a diverse set of replay attacks – photo/video replays on smartphone, and laptop displays under varying brightness levels, distances, and angles—to reflect real-world spoofing scenarios. Designed for researchers and developers working on liveness detection, this dataset provides broad coverage for training and validating anti-spoofing models, delivering end-to-end completeness for iBeta Level 1
iBeta Level 1 dataset summary
The dataset combines two types of attack to deliver full iBeta Level 1 PAD coverage:
Paper attacks: 22,000+ paper-mask/print attacks from 80+ participants with a balanced mix of genders and ethnicities. Each sequence is captured on both iOS and Android, with varied viewpoints and multi-frame 10-second videos for active liveness detection
- Replay attacks (combined, mobile + PC): Over 8,000 replay-display clips derived from selfies of 2,500+ people across two sources. Replays are performed on smartphone screens (iOS/Android), laptops, and desktop monitors/PC displays
Together, the corpus offers 22k+ paper and 8k+ replay attack videos across diverse people, devices, screens, lighting conditions, and capture geometries—providing broad, Level 1 complete coverage for training and validating liveness/anti-spoofing models
Source and collection methodology
Data were collected from real-life selfies and short videos provided by participants, followed by two families of presentation attacks
- Paper attacks: print, cutout, cylinder, and 3D mask variations, recorded on both iOS and Android with controlled changes in angle, distance, and lighting
- Replay attacks (mobile + PC): photos/videos of the same participants replayed on smartphone screens (iOS/Android) and desktop monitors. Replay clips (~5–12+ s) include slow camera motion, zoom-in/zoom-out phases, varied brightness, viewing angles, and distances; phone borders are hidden when applicable
All sequences contain explicit zoom-in and zoom-out segments to support active liveness detection and to simulate realistic spoofing attempts
How companies achieved iBeta with us
This dataset is ideal for teams focused on liveness detection and PAD model training. It’s especially valuable for developers preparing their models for iBeta certification, as it includes a comprehensive set of spoofing scenarios required for level 1 testing
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
iBeta Level 1 dataset features
Participants & scale: 80+ paper-attack participants; 2,500+ selfie contributors for replay
Coverage: 22k+ paper attacks + 8k+ replay clips (combined)
Devices & screens: iOS/Android phones (15 models) + laptops/desktop monitors (PC)
Capture details: multi-frame videos — paper ~10 s, replay ~5–12+ s; explicit zoom-in/zoom-out for active liveness
Diversity: balanced mix of genders and ethnicities (Caucasian, Black, Asian) with varied lighting, angles, and distances
Why Axonlabs better than competitors
One of our partners tested our dataset and a competitor’s dataset using their own liveness detection model while preparing for iBeta Level 1 certification. The results show a clear difference in difficulty between the two datasets. Both datasets were tested on a sample of approximately 200 attack attempts each, ensuring a fair comparison
• Our dataset presents a greater challenge for liveness detection models. The model frequently misclassified attack images as real (label 0), meaning our spoofing techniques are more advanced and harder to detect
• The competitor’s dataset, on the other hand, was mostly detected as attacks (score 1), except for a single type of attack where the model showed some uncertainty
This demonstrates that our dataset provides more value for training robust liveness detection models, as it exposes them to more deceptive and realistic attacks
Understanding the score:
Horizontal axis: score value (0 – model judges the frame as “live”, 1 – “spoof”). Dot color shows ground truth: green = genuine face, red = spoof attack
By training on a more challenging dataset, models can significantly improve their spoof detection capabilities, making them more resilient against real-world threats
This dataset provides 5 variations of spoof attacks
Some of the spoof attacks in our dataset were tested on Doubango, a leading 3D liveness detection framework
Doubango performs advanced 3D liveness checks using a single 2D image and claims to outperform market leaders like FaceTEC, BioID, Onfido, and Huawei in both speed and accuracy
During testing, our attack images bypassed Doubango’s security checks, with the system generating green bounding boxes around the faces (indicating acceptance as “live” users). This confirms that the attacks were not flagged as spoofs, demonstrating their ability to trick even high-performance systems
These results highlight the quality of our dataset for training robust anti-spoofing models capable of defending against evolving threats in real-world scenarios
1. Real life selfie & videos from participants
Genuine facial data collected in various lighting conditions and angles to ensure robust system evaluation
2. Print and Cutout paper attacks
Attackers use printed photos or cutout masks with eye mouth holes to trick recognition systems
3. Cylinder attack to create volume effect
A printed face is wrapped around a cylindrical object to simulate a 3D structure. This method is effective in deceiving simple 2D detection algorithms
4. Paper attacks on Actor with head/eyes variations
A paper face is placed over a real person’s head to mimc real facial movement. Variation include blinking, head tilts, and expressions to test system resilience
5. 3D paper masks with volume based elements such as nose
High-quality 3D masks icorporate raised features sucj as a nose to enhance realism. More challenging for liveness detection algorithms
Download information
Sample data are available on Kaggle as three separate datasets: Paper Attacks (sample), Replay Attacks — Mobile (sample), and Replay Attacks — PC/Laptop (sample). Request full access or additional samples via 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
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Our collection includes many datasets for various requests
iBeta Level 1 Dataset
– 22,000+ videos
– 80+ participants
– zoom in and
zoom out
iBeta Level 2 Dataset
– 25 000+ videos
– 3D masks
– iBeta Level 2
Replay Display attacks
– 5,000+ videos
– 1,000+ participants
– Balanced mix of genders and ethnicities
Photo Print Dataset
– 7000+ videos.
– 10-20 second each video
– Mix of genders
