用于远程身份文件全息图检测的视频Transformer
Video Transformer for Remote Identity Document Hologram Detection
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中文总结 AI 辅助
针对远程身份文件认证难题，提出基于视频Transformer的远程身份文件验证系统（RIDVS），用于检测智能手机拍摄视频中的全息图，该方法优于现有方法，在中小型数据集训练下也有高准确率，还评估了模型对节俭性的适应性。
中文摘要 AI 辅助
多年来，使用身份证件进行远程身份认证一直是一项重大挑战。深度伪造技术的出现和人工智能引导工具的发展，帮助欺诈者制造伪造身份证件。确保身份证件的真实性已成为远程认证安全的关键线索。鉴于行政和交易流程的数字化程度不断提高，这一需求更加迫切。为确保广泛的可及性，系统应仅依赖通过移动设备捕获的视频。在这种特定背景下，确认身份证件的真实性是一项真正的挑战，因为许多安全特征需要特定设备，如红外传感器。全息印刷是未充分利用但很有前景的安全特征之一，难以伪造，根据光照产生独特视觉效果，在智能手机摄像头拍摄的视频中可检测且难以模仿。本文提出一种远程身份文件验证系统（RIDVS）和一种基于视频Transformer的方法，用于检测智能手机拍摄的简单视频中的全息图。该系统专为基于智能手机的捕获过程设计，随后进行服务器端验证。全息图检测方法基于先前在相关研究领域验证的强大模型。实验表明，该方法优于现有SotA方法，即使在中小型数据集上训练也能实现近乎完美的准确率。特别是，与最佳MIDV-Holo基线相比，召回率提高了26.86%，准确率提高了17.93%。本研究还包括多个实验，评估模型在训练样本和计算资源方面对节俭性的适应性。
英文摘要
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.
发表机构
- La Rochelle University（拉罗谢尔大学）
机构由 AI 辅助整理，请以论文原文为准。
