The Third Competition on Document Forgery Detection on ID-Cards and Passports
Computer Vision and Pattern Recognition
Summary
The authors analyzed results from the Third International Competition on detecting fake ID cards and passports. They organized two parts: one using synthetic data under controlled conditions, and another with various real attack types. The team named Incode won both parts by building reliable systems that work well across different scenarios. The competition showed that successful fake detection needs to be accurate and consistent in many situations. Over 63 teams participated, making this event an important benchmark for secure identity verification.
Authors
Juan E. Tapia, Mario Nieto, Juan M. Espin, Álvaro S. Rocamora, Javier Barrachina, Naser Damer, Christoph Busch
Abstract
This paper presents a comprehensive analysis of the results from the Third International Competition on Document Forgery Detection on ID-Cards and Passports, which was held across two distinct tracks. Track 1 evaluates a synthetic-data-based ID-PAD system under controlled but diverse conditions, where the winning team, \textit{Incode}, achieves an $AV_{Rank}$ of 27.82%, confirming consistent performance across metrics and highlighting the importance of a balanced, generalizable design. In Track 2, the challenge intensifies with heterogeneous attack scenarios across different domains, where \textit{Incode} again achieved the top position with an $AV_{Rank}$ of 68.71% across thresholds, outperforming some baselines and established methods. These results demonstrate that PAD effectiveness requires not only high accuracy but also consistency across diverse attack types and imaging conditions. The success of this initiative across both tracks underscores the value of collaboration between companies and academic teams. This year, more than \textit{63 teams} were registered, and more than \textit{100 submission models} were evaluated. This competition has evolved into a leading benchmark state-of-the-art in PAD on ID documents, setting the standard for performance, reproducibility, and real-world applicability in secure identity verification.