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Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 10, 2017
Summary
This study introduces a new statistical model to better assess multibiometric system security against presentation attacks. The model accurately predicts system performance, even against novel attacks, improving fusion rule design.
Area of Science:
- Biometrics
- Cybersecurity
- Machine Learning
Background:
- Multibiometric systems are vulnerable to presentation attacks.
- Existing models overestimate vulnerability by assuming fake scores match genuine scores.
- This leads to ineffective fusion rules and security overestimation.
Purpose of the Study:
- To propose a statistical meta-model for face and fingerprint presentation attacks.
- To characterize a wider range of fake score distributions, including unknown attacks.
- To enable thorough security evaluation and uncertainty analysis of multibiometric systems.
Main Methods:
- Developed a statistical meta-model for presentation attacks.
- Incorporated uncertainty analysis for vulnerability assessment.
- Designed secure fusion rules based on the proposed model.
Main Results:
- The model reliably predicts multibiometric system performance against known and novel attacks.
- Fusion rules designed with the model show improved performance trade-offs under attack.
- The approach quantifies vulnerability variations against diverse attack types.
Conclusions:
- The proposed meta-model enhances the security evaluation of multibiometric systems against presentation attacks.
- It allows for the design of more robust fusion rules, improving overall system security.
- The method shows potential for extension to other biometric modalities.
