The Third Competition on Document Forgery Detection on ID-Cards and Passports

2026-07-17

AI Summary

This study addresses the challenge of anti-spoofing detection for identity documents and passports under diverse forgery attacks and complex imaging conditions. The authors introduce the first large-scale evaluation benchmark encompassing both synthetic and real heterogeneous attacks, featuring two tracks: Track 1 evaluates model performance on synthetically generated data with controlled diversity, while Track 2 assesses generalization capability against cross-domain real-world attacks. The work proposes an ID-PAD system leveraging synthetic data, a multi-threshold consistency evaluation protocol, and a cross-domain attack simulation methodology. The Incode team achieved top performance among 63 participating teams, with AV_Rank scores of 27.82% and 68.71%, establishing this benchmark as a leading standard in document anti-spoofing research.

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.

Problem

• Document Forgery Detection
• ID-PAD
• Identity Verification
• Heterogeneous Attacks
• Biometric Security

Innovation

• Document Forgery Detection
• Presentation Attack Detection (PAD)
• Synthetic Data
• Cross-domain Generalization
• Benchmarking