SynID: Passport Synthetic Dataset for Presentation Attack Detection

TL;DR

This paper introduces SynID, a synthetic passport dataset created by combining synthetic data and open-access information to improve presentation attack detection for ID documents, addressing data scarcity issues.

Contribution

The paper presents a novel hybrid dataset generation method for passports, enhancing training resources for PAD systems in remote verification scenarios.

Findings

The dataset enables better training of PAD models.

Synthetic data improves detection accuracy.

Realistic images enhance model robustness.

Abstract

The demand for Presentation Attack Detection (PAD) to identify fraudulent ID documents in remote verification systems has significantly risen in recent years. This increase is driven by several factors, including the rise of remote work, online purchasing, migration, and advancements in synthetic images. Additionally, we have noticed a surge in the number of attacks aimed at the enrolment process. Training a PAD to detect fake ID documents is very challenging because of the limited number of ID documents available due to privacy concerns. This work proposes a new passport dataset generated from a hybrid method that combines synthetic data and open-access information using the ICAO requirement to obtain realistic training and testing images.