CONF
Chingovska_CVPRWORKSHOPONBIOMETRICS_2013/IDIAP
Anti-spoofing in action: joint operation with a verification system
Chingovska, Ivana
Anjos, André
Marcel, Sébastien
biometric recognition
Counter-Measures
Fusion
Spoofing
trustworthy
vulnerability
EXTERNAL
https://publications.idiap.ch/attachments/papers/2013/Chingovska_CVPRWORKSHOPONBIOMETRICS_2013.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/Chingovska_Idiap-RR-19-2013
Related documents
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Biometrics
Portland, Oregon
2013
Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses and attacks. However, spoofing counter-measures are not designated to operate stand-alone, but as a part of a recognition system they will protect. In this paper, we study techniques for decision-level and score-level fusion to integrate a recognition and anti-spoofing systems, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently. By doing so, we are able to report the impact of different spoofing counter-measures, fusion techniques and thresholding on the overall performance of the final recognition system. For a specific use-case covering face verification, experiments show to what extent simple fusion improves the trustworthiness of the system when exposed to spoofing attacks.
REPORT
Chingovska_Idiap-RR-19-2013/IDIAP
Anti-spoofing in action: joint operation with a verification system
Chingovska, Ivana
Anjos, André
Marcel, Sébastien
Anti-spoofing
Counter-Measures
recognition
security
verification
EXTERNAL
https://publications.idiap.ch/attachments/reports/2013/Chingovska_Idiap-RR-19-2013.pdf
PUBLIC
Idiap-RR-19-2013
2013
Idiap
May 2013
Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses and attacks. However, spoofing counter-measures are not designated to operate stand-alone, but as a part of a recognition system they will protect. In this paper, we study techniques for decision-level and score-level fusion to integrate a recognition and anti-spoofing systems, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently. By doing so, we are able to report the impact of different counter-measures, fusion techniques and thresholding on the overall performance of the final recognition system. For a specific use-case covering face verification, experiments show to what extent simple fusion improves the trustworthiness of the system when exposed to spoofing attacks.