Finger Vein Spoof GANs: Is Synthesis Using Diffusion or VisionTransformer Superior for Presentation Attack Detector Training?
Andreas Vorderleitner, Andreas Uhl · 2025
Four traditional GAN-based I2I translation techniques for unpaired data have been employed for the synthesis of biometric finger vein presentation attack instrument (PAI) samples in earlier work (three public presentation attack datasets have been considered). These synthetic samples have been used to train presentation attack detectors (PAD). Here we extend this work by using more recent image synthesis techniques to generate the required attack sample data, i.e. StyleSwin and DDPM diffusion. We aim to assess if these more recent techniques are able to outperform the classical GAN techniques when used as training data (using DenseSIFT feature sets in their PAD classifier). Our analysis reveals that, contrasting to expectations, PAD accuracy is on par with the traditional GAN-based synthesis techniques under the restrictive conditions of our evaluation (in particular the severely limited size of available training data).