  In cancellable biometrics (CB) schemes, template security is achieved by
applying, mainly non-linear, transformations to the biometric template. The
transformation is designed to preserve the template distance/similarity in the
transformed domain. Despite its effectiveness, the security issues attributed
to similarity preservation property of CB are 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 are 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 interpret the effectiveness of
CSA from the supervised learning perspective. We identify such constraints then
conduct extensive experiments to demonstrate CSA against CB with LFW face
dataset. The results suggest that CSA is effective to breach IoM hashing and
BioHashing security, and outperforms GASA significantly. Inferring from the
above results, we further remark that, other than IoM and BioHashing, CSA is
critical to other CB schemes as far as the constraints can be formulated.
Furthermore, we reveal the correlation of hash code size and the attack
performance of CSA.
