用递归模型实现低数据量下伪造证件的精准识别
Recurrent Few-Shot model for Document Verification
- 采用递归结构提升对低分辨率文档的鲁棒性
- 在少样本条件下仍能识别未见过的证件类型
- 适合数据稀缺场景下的证件真伪验证任务
通用身份或旅行证件的图像与视频验证系统尚未达到可信赖的性能水平。影响其表现的因素包括图像和视频分辨率低以及训练数据不足。尤其在面对未见过的证件类别时，挑战更为严峻。本文提出一种基于递归结构的少样本模型，用于在少样本场景下检测伪造证件。该递归架构增强了模型对文档分辨率变化的适应能力，而少样本机制使模型在未见证件类别上仍能保持良好性能。在SIDTD和Findit数据集上的初步实验结果表明，该模型在该任务中表现出色。
原文摘要 · Abstract (English)
General-purpose ID, or travel, document image- and video-based verification systems have yet to achieve good enough performance to be considered a solved problem. There are several factors that negatively impact their performance, including low-resolution images and videos and a lack of sufficient data to train the models. This task is particularly challenging when dealing with unseen class of ID, or travel, documents. In this paper we address this task by proposing a recurrent-based model able to detect forged documents in a few-shot scenario. The recurrent architecture makes the model robust to document resolution variability. Moreover, the few-shot approach allow the model to perform well even for unseen class of documents. Preliminary results on the SIDTD and Findit datasets show good performance of this model for this task.
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