Nowadays with the development of photosensitive seal technology, the seal fraud events have gradually increased. Forged seals can bring considerable benefits to counterfeiters, and will also bring huge losses to companies and users. Since it is almost impossible to collect enough forged seal samples, traditional machine learning methods do not work in this situation. In this paper, a method based on PU learning and distance learning is proposed. This method uses a limited number of labeled samples and some unlabeled samples to train multiple kNN classifiers to identify forged seal imprints, and use distance learning to improve the performance of kNN classifiers. The experimental results show that the F1-score of the proposed method can reach 0.97 regardless of the seal imprints with lots of text background noise, which outweighs many traditional models.

随着光敏印章技术的发展，印章欺诈事件逐渐增多。伪造的印章可以给造假者带来可观的收益，也将给公司和用户带来巨大的损失。由于几乎不可能收集到足够的伪造的密封样本，因此传统的机器学习方法在这种情况下不起作用。本文提出了一种基于PU学习和远程学习的方法。该方法使用有限数量的标记样本和一些未标记样本来训练多个kNN分类器以识别伪造的印记印记，并使用远程学习来改善kNN的性能分类器。实验结果表明，该方法的F1得分可以达到0.97，而与带有大量文本背景噪声的印记无关，这比许多传统模型都高。