Kin-Wolf: Kinship-established Wolfs in Indirect Synthetic Attack

Two common attacks against biometric systems are direct (or physical) access and indirect (or logical) access. While most detection techniques focus on the former, often called presentation attacks, that occur at pre-sensor level, the attack surface for indirect access, that takes place post-sensor, is larger. In this paper, an indirect attack in the realm of faces is explored that utilizes a unique soft-biometric feature called ‘Kinship Cues’. Unlike gender and ethnicity, kinship is less explored but powerful; we find that its knowledge can significantly increase the chances of an attacker getting access to a system. Due to lack of kin data in other domains, our attack is only performed against facial biometric systems. Nevertheless, the results underscore the impact of kinship cues and their need to be investigated in other domains such as fingerprint and iris. This kinship artifact boosts the convergence speed of state-of-the-art iterative adaptive Bayesian hill climbing attacks. Further, it is exploited to generate a dictionary of input images, commonly called wolf images, in a novel kinship-based non-iterative indirect attack that we call Kin-Wolf. A classical image fusion technique (morphing) and a deep learning based kinship framework utilizing pre-trained StyleGAN2 are investigated to generate the wolf images. The trade-off between kinship cues and randomization is also studied and a 6× average improvement in attack accuracy is achieved for Kin-Wolf over random probes.