用Stable Diffusion生成身份证真样本，提升伪造检测系统泛化能力
ID-Card Synthetic Generation: Toward a Simulated Bona fide Dataset
- 基于Stable Diffusion生成仿真真卡图像，填补真实样本不足
- 生成图像被检测系统识别为真卡，提升检测性能
- 适合需要扩充真实数据的PAD系统研究者使用
当前身份证伪造攻击检测（PAD）系统面临真实图像样本稀缺与攻击手段多样化双重挑战。现有方法多聚焦于生成攻击样本，却忽视了真实样本的匮乏问题。本文首次提出利用Stable Diffusion生成仿真真卡图像的方法，以增强检测器的泛化能力。生成图像在从零训练的系统及商用解决方案中均被正确识别为真卡，有效缓解数据限制，显著提升检测性能。该方法为构建更鲁棒的PAD系统提供了新路径。
原文摘要 · Abstract (English)
Nowadays, the development of a Presentation Attack Detection (PAD) system for ID cards presents a challenge due to the lack of images available to train a robust PAD system and the increase in diversity of possible attack instrument species. Today, most algorithms focus on generating attack samples and do not take into account the limited number of bona fide images. This work is one of the first to propose a method for mimicking bona fide images by generating synthetic versions of them using Stable Diffusion, which may help improve the generalisation capabilities of the detector. Furthermore, the new images generated are evaluated in a system trained from scratch and in a commercial solution. The PAD system yields an interesting result, as it identifies our images as bona fide, which has a positive impact on detection performance and data restrictions.
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