Verification of Dynamic Holographic Behavior in Identity Documents

2026-07-07

AI Summary

This work addresses the challenge that existing methods struggle to effectively detect dynamic attacks—such as handcrafted forgeries of optically variable devices (OVDs) on identity documents—and the absence of publicly available datasets in this domain. To overcome these limitations, the paper proposes a novel approach capable of verifying OVD authenticity without requiring training on dynamic attack samples. The key contributions include the release of MIDV-DynAttack, the first public dataset containing real-world dynamic attacks; the design of a generalizable verification algorithm based on visual dynamic behavior analysis; and the establishment of a unified evaluation benchmark. Experimental results demonstrate that the proposed method significantly outperforms current techniques on the new dataset and exhibits superior generalization capability, particularly against unseen dynamic attack scenarios.

Abstract

This paper addresses the remote verification of the authenticity of Optically Variable Devices (commonly known as holograms) on identity documents. Typically placed over the cardholder's photo, these devices provide strong and easily verifiable security for human inspection but pose challenges for automated verification. Existing approaches easily cover static frauds (e.g. paper photocopy) and can be evaluated for such, but their capacity to detect real, dynamic fraud cases (e.g. handcrafted hologram) has not been evaluated to date because of the lack of public datasets. Furthermore, they are usually trained to detect known attack types, and few of them can generalize to new, unseen attacks. This work features three contributions to address these limitations: 1) a new public dataset, MIDV-DynAttack, which extends the existing MIDV-Holo dataset with realistic, static and dynamic attacks against identity document specimens, tripling the number of attack samples compared to the original dataset, 2) a novel verification method which can assess the authenticity of a specific hologram thanks to the analysis of its dynamic behavior and appearance, can be trained without dynamic attack samples, and exhibits new state-of-the-art performance, 3) a benchmark of existing approaches which follows a clear evaluation protocol and emphasizes the inability of other approaches to deal with dynamic attacks, as well as new challenging attacks to deal with. Code and dataset are publicly available at https://github.com/EPITAResearchLab/pouliquen.25.icdar.

Problem

• Optically Variable Devices
• hologram verification
• dynamic attacks
• identity document authentication
• anti-fraud

Innovation

• dynamic hologram verification
• Optically Variable Devices
• MIDV-DynAttack dataset
• generalization to unseen attacks
• identity document security