1 research outputs found
Security analysis of cancellable biometrics using constrained-optimized similarity-based attack
Cancellable biometrics (CB) intentionally distorts biometric template for
security protection, and simultaneously preserving the distance/similarity for
matching in the transformed domain. Despite its effectiveness, the security
issues attributed to similarity preservation property of CB is underestimated.
Dong et al. [BTAS'19], exploited the similarity preservation trait of CB and
proposed a similarity-based attack with high successful attack rate. The
similarity-based attack utilizes preimage that generated from the protected
biometric template for impersonation and perform cross matching. In this paper,
we propose a constrained optimization similarity-based attack (CSA), which is
improved upon Dong's genetic algorithm enabled similarity-based attack (GASA).
The CSA applies algorithm-specific equality or inequality relations as
constraints, to optimize preimage generation. We justify the effectiveness of
CSA from the supervised learning perspective. We conduct extensive experiments
to demonstrate CSA against Index-of-Max (IoM) hashing with LFW face dataset.
The results suggest that CSA is effective to breach IoM hashing security, and
outperforms GASA remarkably. Furthermore, we reveal the correlation of IoM hash
code size and the attack performance of CSA