With the emergence of big data, deep learning (DL) approaches are becoming quite popular in many branches of science. Forensic science is no longer an exception. However, there are certain problems in forensic science where the solutions would hardly benefit from the recent advances in DL algorithms. Document authentication is one such problem where we can have many reference samples, and with the big data scenario probably we would have even more number of reference samples but number of defective or forged samples will remain an issue. Experts often encounter situations where there is no or hardly a scanty number of forged samples available. In such situation, employment of data-hungry algorithms would be inefficient as they will not be able to learn the forged samples properly. This paper addresses this problem and proposes a novel reference modelling framework for forensic document authentication. The approach is based on Mahalanobis space. Two questioned document examination problems have been studied to show the effectiveness of our reference modelling algorithm which has also been compared to a commonly used learning approach, namely neural network-based classification.

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甚至大数据也是不够的：需要用于司法鉴定的新颖参考模型

随着大数据的出现，深度学习（DL）方法在许多科学分支中变得非常流行。法医学不再是一个例外。但是，法医科学存在某些问题，这些解决方案几乎无法从DL算法的最新进展中受益。文档认证就是这样一个问题，我们可以有很多参考样本，在大数据场景下，我们可能会有更多的参考样本，但是有缺陷或伪造的样本数量仍然是一个问题。专家经常遇到没有或几乎没有伪造样品的情况。在这种情况下，需要大量数据的算法的效率很低，因为它们将无法正确学习伪造的样本。本文解决了这个问题，并提出了一种新颖的司法鉴定文档参考模型框架。该方法基于马氏距离。研究了两个有问题的文档检查问题，以显示我们的参考建模算法的有效性，该算法也已与一种常用的学习方法（即基于神经网络的分类）进行了比较。