iBeta Level 1 Paper Attacks Dataset

Comprehensive dataset for PAD with 22,000+ iBeta level 1 attacks

Check samples on Kaggle

Introduction

The iBeta Level 1 Paper Attacks Dataset provides a comprehensive collection of paper-based spoofing attacks specifically designed for Presentation Attack Detection (PAD) testing at iBeta Level 1. This dataset is tailored for researchers and developers working on liveness detection, offering a wide range of paper mask attack variations to aid in training AI models for anti-spoofing applications

Dataset summary

The dataset includes over 22,000 paper mask attacks performed by 80+ participants, with a balanced representation across gender and ethnicity (Caucasian, Black, and Asian). Each attack sequence is recorded on both iOS and Android devices, offering varied perspectives and multi-frame, 10-second videos to support active liveness detection

Source and collection methodology

The data collection process involved real-life selfies and videos from participants, followed by multiple paper attack types, such as print, cutout, cylinder, and 3D mask attacks. Each video includes zoom-in and zoom-out phases to enhance the dataset’s application in active liveness detection, simulating realistic spoofing attempts

Use cases and applications

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

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

80+ Participants engaged in the project
Each attack is captured on iOS and Android phone, multiple frames, ~10 sec videos
Diverse Representation: Balanced mix of genders and ethnicities with Caucasian, Black and Asian ethnicity
22.000+ Paper mask attacks on the participants
Zoom in and zoom out phase for Active Liveness
This dataset can be integrated into This dataset can be integrated into bundles

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:

• Score = 1 → Attack detected (label 1, red dots)

• Score = 0 → Model thinks it’s a real person (label 0, green dots)

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

A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below

Have a question?

Where does the data come from?

We collect data from our internal team. All information is further verified by our specialists

What is the process?

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

Can you help us meet dataset disclosure requirements, GDPR and other regulatory controls?

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.

What makes this dataset suitable for iBeta certification?

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

What is the price of the dataset?

The price depends on your specific requirements. Please submit a request to receive a free consultation

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