Video Transformer for Remote Identity Document Hologram Detection

2026-07-13

AI Summary

This work addresses the vulnerability of existing remote identity verification methods, which struggle to effectively authenticate security features such as holograms on identity documents using standard smartphone video and are thus susceptible to AI-generated forgeries. To overcome this limitation, the paper proposes the first remote document verification system based on a video Transformer architecture. By capturing short videos with off-the-shelf smartphones and modeling the dynamic visual characteristics of holograms under varying illumination conditions on the server side, the method enables robust authenticity discrimination. Notably, it introduces video Transformers to hologram detection in mobile video for the first time, achieving substantial performance gains and enhanced generalization under low-data, low-resource settings. Experimental results demonstrate significant improvements over the current state-of-the-art baseline MIDV-Holo, with a 26.86% increase in recall and a 17.93% gain in accuracy, attaining near-perfect detection performance on medium-scale datasets.

Abstract

Remote identity authentification using Identification Documents has been a major challenge for several years. DeepFakes advent and the development of AI-guided tools helps fraudsters creating counterfeit ID Documents. Ensuring the authenticity of ID Documents has become a real clue in the seurization of remote authentification. This need is all the more pressing given the increasing digitization of administrative and transactional processes. To ensure widespread accessibility, the system should rely solely on video captured via mobile devices. In this specific context, confirming the authenticity of ID is a real challenge as many security features needs specific device like infrared sensor for instance. Among underutilized but promising security features, holographic printings hold a special place. Difficult to counterfeit, they produce distinctive visual effects according enlightment, making them both detectable in a video captured by a smartphone camera and difficult to imitate. In this paper, we propose a Remote Identity Document Verification System (RIDVS) and an approach based on a video transformer for detecting holograms in simple videos captured by smartphones. Our system is designed for a smartphone-based capture process, followed by a server-side verification. The hologram detection method builds on a robust model previously validated in a related research domain. We demonstrate that it outperforms existing SotA methods, achieving near-perfect accuracy even when trained on medium- to small-sized datasets. In particular, we report improvements of +26.86\% in Recall and +17.93\% in accuracy over the best MIDV-Holo baseline. This study includes several experiments that evaluate the model adaptation to frugality, both for training samples and computational resources.

Problem

• hologram detection
• remote identity verification
• ID document authentication
• video-based authentication
• smartphone capture

Innovation

• Video Transformer
• Hologram Detection
• Remote Identity Verification
• ID Document Authentication
• Mobile-based Capture