  The accurate detection of ID card Presentation Attacks (PA) is becoming
increasingly important due to the rising number of online/remote services that
require the presentation of digital photographs of ID cards for digital
onboarding or authentication. Furthermore, cybercriminals are continuously
searching for innovative ways to fool authentication systems to gain
unauthorized access to these services. Although advances in neural network
design and training have pushed image classification to the state of the art,
one of the main challenges faced by the development of fraud detection systems
is the curation of representative datasets for training and evaluation. The
handcrafted creation of representative presentation attack samples often
requires expertise and is very time-consuming, thus an automatic process of
obtaining high-quality data is highly desirable. This work explores ID card
Presentation Attack Instruments (PAI) in order to improve the generation of
samples with four Generative Adversarial Networks (GANs) based image
translation models and analyses the effectiveness of the generated data for
training fraud detection systems. Using open-source data, we show that
synthetic attack presentations are an adequate complement for additional real
attack presentations, where we obtain an EER performance increase of 0.63%
points for print attacks and a loss of 0.29% for screen capture attacks.
