Real-world attack datasets

Custom data collection for biometric systems, focused on real attack scenarios and production-level conditions

built around your system — not generic data

We focus on collecting data for specific failure scenarios and real-world conditions relevant to your system

Focus on attack conditions and edge cases

Designed for models improvement

Adapted to real system behavior

Scenario-driven data collection

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Problems & challenges

Typical challenges we help solve

Weakness on edge cases
Model performance drops on rare, complex, or atypical real-world scenarios
Limited attack coverage
The dataset includes insufficient attack scenarios, reducing robustness against adversarial or harmful inputs
Misaligned training data
Training data does not accurately reflect real production behavior or user interactions
Production-aligned data
Data collection is designed around your production pipeline and mirrors real user behavior
Environment & device specific
Data is collected across relevant environments and devices to match real usage conditions

Process

Define target scenarios
We align on specific cases and conditions relevant to your system
Design data collection approach
We structure the data collection around real-world usage and attack scenarios
Execute data collection
Data is collected across controlled and variable conditions
Deliver structured dataset
Data is prepared for direct use in ML pipelines

Define target scenarios
We align on specific cases and conditions relevant to your system
Design data collection approach
We structure the data collection around real-world usage and attack scenarios
Execute data collection
Data is collected across controlled and variable conditions
Deliver structured dataset
Data is prepared for direct use in ML pipelines

Main directions