Finger Vein Spoof GANs: Can We Supersede the Production of Presentation Attack Artefacts?

GAN-based I2I translation techniques for unpaired data are employed for the synthesis of biometric finger vein presentation attack instrument samples corresponding to three public presentation attack datasets. For the assessment of these synthetic samples, we analyse their behaviour when attacking finger vein recognition systems, comparing these results to such obtained from actually crafted presentation attack samples. We observe that although visual appearance and sample set correspondence are surprisingly good for some networks, respectively, the assessment of the behaviour of the data in a conducted attack is more difficult. Even if for some recognition schemes out of 11 considered we find a good accordance in terms of IAPMR (for many we don’t), the attack score distributions turn out to be highly dissimilar when comparing crafted and synthetic presentation attack instrument samples. More work is needed to be able to correctly interpret corresponding diverging results with respect to the relevance in attack simulation. From the seven network architectures considered, CycleGAN provides the most useful results, but the artificially created samples do not fully mimic the behaviour of crafted ones.