Video Transformer for Remote Identity Document Hologram Detection

• URL: http://arxiv.org/abs/2607.11419v1
• Date: Mon, 13 Jul 2026 11:24:54 GMT
• Title: Video Transformer for Remote Identity Document Hologram Detection
• Abstract summary: hologram detection method builds on a robust model previously validated in a related research domain.<n>Our system is designed for a smartphone-based capture process, followed by a server-side verification.
• Score: 1.1688548469063844
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• EDVD-LLaMA: Explainable Deepfake Video Detection via Multimodal Large Language Model Reasoning [58.42596067220998]
  deepfake video technology has not only facilitated artistic creation but also made it easier to spread misinformation.<n>Traditional deepfake video detection methods face issues such as a lack of transparency in their principles and insufficient capabilities to cope with forgery techniques.<n>This paper proposes the explainable deepfake video detection (EDVD) task and designs the EDVD-LLaMA multimodal reasoning framework.
  arXiv  Detail & Related papers  (2025-10-18T10:34:05Z)
• Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2) [13.726589034127025]
  We propose and discuss a patch-based methodology to preserve privacy in fake ID detection research.<n>We provide a new public database, FakeIDet2-db, comprising over 900K real/fake ID patches extracted from 2,000 ID images.<n>We present a new privacy-aware fake ID detection method, FakeIDet2.
  arXiv  Detail & Related papers  (2025-08-14T17:30:36Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images [29.349824933680956]
  We present a deepfake detection algorithm specifically designed for electronic Know Your Customer (eKYC) systems.<n>Our approach evaluates the video's authenticity by detecting temporal inconsistencies in identity vectors extracted by face recognition models.<n>In addition to processing video input, the algorithm utilizes a registered image (assumed to be genuine) to calculate identity discrepancies.
  arXiv  Detail & Related papers  (2025-07-30T12:16:27Z)
• SynID: Passport Synthetic Dataset for Presentation Attack Detection [7.1212970088491385]
  Increase is driven by several factors, including the rise of remote work, online purchasing, migration, and advancements in synthetic images.<n>This work proposes a new passport dataset generated from a hybrid method that combines synthetic data and open-access information.
  arXiv  Detail & Related papers  (2025-05-12T13:24:54Z)
• In Anticipation of Perfect Deepfake: Identity-anchored Artifact-agnostic Detection under Rebalanced Deepfake Detection Protocol [20.667392938528987]
  We introduce the Rebalanced Deepfake Detection Protocol (RDDP) to stress-test detectors under balanced scenarios. We present ID-Miner, a detector that identifies the puppeteer behind the disguise by focusing on motion over artifacts or appearances.
  arXiv  Detail & Related papers  (2024-05-01T12:48:13Z)
• Weakly Supervised Training for Hologram Verification in Identity Documents [1.9197225657187418]
  We propose a method to verify the authenticity of Optically Variable Devices (OVDs) in identity documents. Our method processes video clips captured with smartphones under common lighting conditions. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance.
  arXiv  Detail & Related papers  (2024-04-26T08:47:28Z)
• Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis [69.09526348527203]
  Deep generative models have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. We propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios.
  arXiv  Detail & Related papers  (2021-05-29T21:22:24Z)
• Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System [103.5604680001633]
  Anti-spoofing against high-resolution rendering replay of paper photos or digital videos remains an open problem. We propose a simple yet effective face anti-spoofing system, termed Aurora Guard (AG)
  arXiv  Detail & Related papers  (2021-02-01T09:17:18Z)
• Identity-Driven DeepFake Detection [91.0504621868628]
  Identity-Driven DeepFake Detection takes as input the suspect image/video as well as the target identity information. We output a decision on whether the identity in the suspect image/video is the same as the target identity. We present a simple identity-based detection algorithm called the OuterFace, which may serve as a baseline for further research.
  arXiv  Detail & Related papers  (2020-12-07T18:59:08Z)
• Towards Precise Intra-camera Supervised Person Re-identification [54.86892428155225]
  Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view. Lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
  arXiv  Detail & Related papers  (2020-02-12T11:56:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.