[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84118-en":3,"doc-seo-84118-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84118,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Verification of Dynamic Holographic Behavior in Identity Documents","The paper investigates remote verification of Optically Variable Devices (OVDs), commonly known as holograms, on identity documents. While holograms support reliable human inspection, automated methods struggle with dynamic, real-world frauds because public datasets and evaluation for such attacks were missing. The work introduces the MIDV-DynAttack dataset, a verification method based on dynamic hologram behavior that can train without dynamic attack samples, and a benchmark with a clear protocol highlighting limitations on dynamic attacks.","arXiv :2607 .06466v 1 [ cs .CV] 7 Jul 2026  \nVerification of Dynamic Holographic Behavior in Identity Documents  \nGlen Pouliquen 1 ,2, Joseph Chazalon2, Guillaume Chiron 1, Thierry Géraud2, and Ahmad Montaser Awal 1  \n1 IDnow Research Center, Cesson-Sévigné, France  \n[name.surname@idnow.io](name.surname@idnow.io)  \n2 EPITA Research Lab. (LRE), Le Kremlin-Bicêtre, France [name.surname@epita.fr](name.surname@epita.fr)  \nAbstract. 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 MIDVHolo 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](https://github.com/EPITAResearchLab/pouliquen).  \n25 .icdar.  \nKeywords: Identity Documents · Datasets · Hologram Verification  \n1 Introduction  \nMany traditional identity providers still rely on physical documents for authentication, essential for scenarios like border control or restricted area access. These documents feature various security elements, both visible and invisible, to prevent forgery. However, automated remote verification is challenging due to the  \n2 Pouliquen et al.  \nFig. 1. Our proposed dataset MIDV-DynAttack extends the original MIDV-Holo dataset with 1200 new attack videos. MIDV-DynAttack is designed for testing purposes over unseen attacks only, and not for model training or calibration.  \nlimited features detectable by commodity cameras on smartphones, leading to potential creative attacks that can bypass existing systems.  \nTo address these attacks, two main directions have been explored: Attack Detection (AD) and Model Verification (MV) . AD centers on identifying subtle indicators that suggest the absence of the physical document, known as Presentation Attack Detection, a well-established field within biometrics [19], addressing issues like screen captures and photocopies. Additionally, AD encompasses the detection of both digital and physical manipulations, including copy-paste forgeries and replacements. This area is thoroughly documented in the literature, featuring numerous dataset publications and benchmarking competitions [20] .  \nVerification of Dynamic Holographic Behavior in Identity Documents 3  \nWhile necessary, AD does not verify the document’s consistency with its expected model. MV, the focus of this paper, checks if a document exhibits the expected security features, detecting forgeries that appear credible but do not conform to the model.  \nIn MV, textual content is often protected by digital signatures, making tampering attempts easily detectable. However, graphical content, such as the bearer’s picture, is harder to secure digitally and","cbCaidQxgau7LXNk","https://ap.wps.com/l/cbCaidQxgau7LXNk","pdf",7384272,1,27,"English","en",105,"# Introduction\n## Related Work\n### Datasets","[{\"question\":\"What problem does the paper address in identity document verification?\",\"answer\":\"It addresses remote authenticity verification of holograms (optically variable devices) on identity documents, especially the difficulty of detecting dynamic, real-world frauds automatically.\"},{\"question\":\"What new dataset is proposed and what does it add?\",\"answer\":\"The paper proposes MIDV-DynAttack, extending MIDV-Holo with realistic static and dynamic handcrafted attacks and tripling the number of attack samples compared with the original dataset.\"},{\"question\":\"How does the proposed verification method work and what data does it require?\",\"answer\":\"It leverages background estimation and suppression to focus on faint holographic behavior and learns an OVD behavior model using pseudo-labels, enabling training without requiring dynamic attack 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problem does the paper address in identity document verification?","Question",{"text":75,"@type":76},"It addresses remote authenticity verification of holograms (optically variable devices) on identity documents, especially the difficulty of detecting dynamic, real-world frauds automatically.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What new dataset is proposed and what does it add?",{"text":80,"@type":76},"The paper proposes MIDV-DynAttack, extending MIDV-Holo with realistic static and dynamic handcrafted attacks and tripling the number of attack samples compared with the original dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed verification method work and what data does it require?",{"text":84,"@type":76},"It leverages background estimation and suppression to focus on faint holographic behavior and learns an OVD behavior model using pseudo-labels, enabling training without requiring dynamic attack 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