  Cancellable biometrics (CB) as a means for biometric template protection
approach refers to an irreversible yet similarity preserving transformation on
the original template. With similarity preserving property, the matching
between template and query instance can be performed in the transform domain
without jeopardizing accuracy performance. Unfortunately, this trait invites a
class of attack, namely similarity-based attack (SA). SA produces a preimage,
an inverse of transformed template, which can be exploited for impersonation
and cross-matching. In this paper, we propose a Genetic Algorithm enabled
similarity-based attack framework (GASAF) to demonstrate that CB schemes whose
possess similarity preserving property are highly vulnerable to
similarity-based attack. Besides that, a set of new metrics is designed to
measure the effectiveness of the similarity-based attack. We conduct the
experiment on two representative CB schemes, i.e. BioHashing and Bloom-filter.
The experimental results attest the vulnerability under this type of attack.
