合成护照数据集解决身份伪造检测训练数据少难题
SynID: Passport Synthetic Dataset for Presentation Attack Detection
- 融合合成数据与公开信息生成真实感护照图像
- 基于ICAO标准构建符合实际的伪造攻击样本
- 适用于远程身份验证中的防伪造系统研发
近年来，远程身份验证系统中对伪造证件检测（PAD）的需求显著上升，主要受远程办公、在线购物、移民及合成图像技术进步推动。同时，针对注册环节的攻击数量激增。由于隐私限制，真实身份证件数据稀缺，导致训练高效PAD模型极为困难。本文提出一种新型护照数据集SynID，采用混合方法结合合成数据与公开可获取信息，并依据ICAO标准生成具有真实感的训练与测试图像，以支持更可靠的伪造检测研究。
原文摘要 · Abstract (English)
The demand for Presentation Attack Detection (PAD) to identify fraudulent ID documents in remote verification systems has significantly risen in recent years. This increase is driven by several factors, including the rise of remote work, online purchasing, migration, and advancements in synthetic images. Additionally, we have noticed a surge in the number of attacks aimed at the enrolment process. Training a PAD to detect fake ID documents is very challenging because of the limited number of ID documents available due to privacy concerns. This work proposes a new passport dataset generated from a hybrid method that combines synthetic data and open-access information using the ICAO requirement to obtain realistic training and testing images.
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