Temporal Modeling of Optically Variable Devices in Identity Documents

• URL: http://arxiv.org/abs/2607.06408v1
• Date: Tue, 07 Jul 2026 15:37:53 GMT
• Title: Temporal Modeling of Optically Variable Devices in Identity Documents
• Abstract summary: We introduce two novel approaches for verifying the dynamic behavior of transparent OVDs protecting the holder's portrait.<n>We demonstrate these approaches can be trained without any attack samples in a self-supervised setting.<n>Our results confirm that modeling temporal dynamics is essential for defeating sophisticated attacks under realistic conditions.
• Score: 2.8297572190956917
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Robust remote verification of identity documents relies on analyzing faint, transparent security features like Optically Variable Devices (OVDs), or "holograms", within user-captured videos under uncontrolled conditions. Current systems, however, face critical limitations: existing methods often treat video frames in isolation, neglecting the intrinsic dynamic nature of OVDs and leaving systems vulnerable to swapping attacks, or focus on general holographic presence and lack the ability to verify specific OVD types. Moreover, the economic infeasibility of frame-by-frame video annotation makes supervised training impractical. In this work, we introduce two novel approaches for verifying the dynamic behavior of transparent OVDs protecting the holder's portrait, specifically designed for open-set scenarios where attack types are unknown during training. We demonstrate that these approaches can be trained without any attack samples in a self-supervised setting, surpassing previous state-of-the-art methods on public datasets while adhering strictly to industrial constraints. Our results confirm that modeling temporal dynamics is essential for defeating sophisticated attacks under realistic conditions, and underscores the promise of sequence modeling and anomaly detection for OVD verification. Code is available at https://github.com/EPITAResearchLab/pouliquen.26.icdar.

Related papers

• VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics [21.438987128589403]
  We introduce textbfmeta-detection into AI-generated video detection, enabling reliable forgery detection.<n>Based on this insight, we propose an automated data construction pipeline that generates paired real-fake videos.<n>Experiments demonstrate that textbfVidForensics-M1 effectively leverages verifiable temporal evidence to achieve robust and generalizable AI-generated video detection.
  arXiv  Detail & Related papers  (2026-08-11T17:58:10Z)
• Verification of Dynamic Holographic Behavior in Identity Documents [1.208889278596182]
  This paper addresses the remote verification of the authenticity of Optically Variable Devices (commonly known as holograms) on identity documents.<n>These devices provide strong and easily verifiable security for human inspection but pose challenges for automated verification.<n>Existing approaches easily cover static frauds and can be evaluated for such, but their capacity to detect real, dynamic fraud cases has not been evaluated to date.<n>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.
  arXiv  Detail & Related papers  (2026-07-07T16:27:28Z)
• From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection [5.170417739307782]
  This survey provides, to our knowledge, the first unified treatment of Presentation Attacks, Digital Injection Attacks, and GenAI-driven synthesis within a single identity threat model.<n>A systematic audit of public datasets from 2019-2025 exposes a persistent Reality Gap between benchmark conditions and operational deployment.<n>We conclude by outlining future directions toward forensically grounded, privacy-preserving, and legally accountable identity verification systems.
  arXiv  Detail & Related papers  (2026-07-01T20:05:46Z)
• Hybrid Robustness Verification for Spatio-Temporal Neural Networks [19.026662893450425]
  Existing verification methods rely on overly conservative approximations or incur prohibitive computational costs.<n>In practice, adversarial perturbations exhibit structured spatial and temporal correlations, constrained to lower-dimensional, semantically meaningful subspaces.<n>We study robustness verification 3D CNNs processing video inputs, targeting applications in action recognition (UCF-101), autonomous driving (Udacity), medical imaging (MedMNIST)
  arXiv  Detail & Related papers  (2026-06-08T17:06:51Z)
• Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions [60.2784641643737]
  Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models.<n>We study the structural identifiability of continuous-time physical laws from raw pixels.<n>We prove that a level-set slope-coverage condition ensures the learned latent space is locally affine to the true physical state.
  arXiv  Detail & Related papers  (2026-05-27T13:22:29Z)
• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Models [2.1198879079315573]
  On-device Vision-Language Models (VLMs) promise data privacy via local execution.<n>We show that the architectural shift toward Dynamic High-Resolution preprocessing introduces an inherent algorithmic side-channel.<n>We demonstrate a dual-layer attack framework against local VLMs.
  arXiv  Detail & Related papers  (2026-03-26T12:53:49Z)
• CtrlAttack: A Unified Attack on World-Model Control in Diffusion Models [92.04182855442254]
  We analyze the vulnerability of I2V models, find that temporal control mechanisms constitute a new attack surface, and reveal the challenge of modeling them uniformly.<n>We propose a trajectory-control attack, called CtrlAttack, to interfere with state evolution during the generation process.<n> Experimental results show that even under low-dimensional and strongly regularized perturbation constraints, our method can still significantly disrupt temporal consistency.
  arXiv  Detail & Related papers  (2026-03-13T08:05:50Z)
• 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)
• T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models [88.63040835652902]
  Text to video models are vulnerable to jailbreak attacks, where specially crafted prompts bypass safety mechanisms and lead to the generation of harmful or unsafe content.<n>We propose T2VShield, a comprehensive and model agnostic defense framework designed to protect text to video models from jailbreak threats.<n>Our method systematically analyzes the input, model, and output stages to identify the limitations of existing defenses.
  arXiv  Detail & Related papers  (2025-04-22T01:18:42Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)

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.