Failure of affine‐based reconstruction attack in regenerating vascular feature points

Abstract

Inverse biometrics methods are a major privacy concern for users of biometric recognition systems. Affine‐based reconstruction attack is an inverse biometrics method that models the biometric recognition algorithm by an affine approximation. This type of attack reconstructs targeted biometric references using the modelled biometric recognition algorithm and the comparison scores issued by the system. Although this reconstruction method has only been successfully applied to reconstruct face images, the common consensus is that any biometric system that issues comparison scores could be vulnerable to such an attack since this method is sufficiently general to be applied to other biometric templates. Here it is shown that the attack fails to regenerate sparse vascular feature point templates. The reconstruction attack on feature point patterns extracted from retina and hand vascular images is tested. The inverse attack match rate for reconstructed reference templates was 0.3% in one experiment using retinal vasculature and 0% for all others. These results show that the reconstruction attack is not as catastrophic as it is widely accepted to be, and that vascular biometric template protection schemes that store sparse templates as references and reveal comparison scores are not susceptible to affine‐based reconstruction attacks.

Protection of Sparse Retinal Templates Using Cohort-Based Dissimilarity Vectors

“…Thus, it is worth evaluating the threat of the MSK reconstruction attack on our proposed system. We have previously showed that this attack fails in reconstructing retinal point pattern templates (the nodes of our graph templates) and retinal images, when point pattern comparison algorithms are applied [18], [19]. Here, we show the attack fails in reconstructing the retinal images and consequently graph pattern when BGC is used as the comparison algorithm.…”

“…6(a) represents the distribution of scores for reconstructed reference images. For 2. https://github.com/mahshidsa/Irreversibility-DissimilarityVector-Retina.git a successful reconstruction, it is expected from the distribution of scores for reconstructed images to overlap with the distribution of scores for mated comparison trials (similar to what was shown for face images in [19]). However, as can be seen in Fig.…”

“…The experiments in this section are similar to the ones in [19], except for the comparison algorithm used in them. In these experiments, we will apply distance scores calculated by the BGC algorithm.…”

“…Previously, we have applied the MSK reconstruction method [17] which applies MDS (Multi-Dimensional Scaling) and an affine approximation to reconstruct biometric samples from comparison scores. We showed that this reconstruction technique is unsuccessful in reconstructing sparse point-pattern templates extracted from retinal vascular patterns [18], and hand vascular patterns and on retinal images [19] where point-pattern comparison scores/algorithms were applied. The same approach is applied in this paper to show that this reconstruction attack has a very low success rate (less than 0.3%) in reconstructing retinal images when the biometric graph comparison (BGC) algorithm [20] is used to compare the sparse graph templates extracted from retinal vasculature.…”