Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style
This work addresses the challenge of data scarcity for training fraud detection systems in digital onboarding, though it is incremental as it builds on existing GAN methods.
The paper tackled the problem of detecting ID card presentation attacks by generating synthetic attack samples using GAN-based image translation models, resulting in a 0.63% improvement in EER for print attacks but a 0.29% loss for screen capture attacks.
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.