用手机视频检测身份证全息图案,防伪效果显著提升。
Video Transformer for Remote Identity Document Hologram Detection

- 基于视频变换器分析手机拍摄的身份证视频,识别全息特征。
- 在中小规模数据集上仍达近完美准确率,召回率提升26.86%。
- 适合移动端身份认证系统,对算力要求低,实用性强。
远程身份认证中,身份证真伪验证面临严峻挑战,尤其是深度伪造与AI工具助长了伪造行为。随着行政与交易流程日益数字化,仅依赖手机拍摄视频进行验证成为刚需。传统安全特征如红外成像需专用设备,难以普及。而全息印刷因难复制且视觉效果随光照变化,适合在普通手机视频中检测。本文提出一种基于视频变换器的远程身份证验证系统(RIDVS),可在手机拍摄、服务器端验证的流程中高效识别全息图案。该方法在已有研究基础上优化,即使在中等至小规模数据集上训练,仍显著超越现有最先进方法,较最佳基准MIDV-Holo提升26.86%的召回率和17.93%的准确率。实验还评估了模型在少量训练样本与低算力下的适应性,验证其在资源受限场景下的可行性。
原文摘要 · Abstract (English)
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.
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