文字篡改笔迹鉴定的法医学性能研究

Priyanka Roy, Soumen Bag
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引用次数: 5

摘要

法律手写文件中的伪造活动是一个可识别的问题。由于存单的微小变更而造成的文件伪造不仅给个人或组织造成巨大的经济损失,而且还会减少一个国家的经济增长。在这里,我们介绍并提出了一种通过分析不同笔的感知相似墨水来检测手写文件伪造的解决方案。本研究是对手写文字的修改进行法医学调查,这种修改是通过添加额外的字母来完成的,从而使单词的整个意思发生变化。该问题被表述为二元分类问题。如果对应文件的文字是由同一支笔书写的,则这些文字被归类为正面类,而文件的文字几乎没有包含字母作为伪造攻击,则被归类为负面类。本文提出了多层感知器分类器,该分类器通过提取Y CbCr基于颜色的统计特征来对计算出来的数据实例进行分类。该方案已在由10支蓝色圆珠笔和10支黑色圆珠笔生成的数据集上进行了测试。得到的平均准确率分别为83.71%和78。18%为蓝笔数据和黑笔数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forensic Performance on Handwriting to Identify Forgery Owing to Word Alteration
Forgery activity in legal handwritten documents is an identifiable problem. Falsification of document due to minute alteration of existings not only causes immense financial loss to a person or to any organization but also lessens the economic growth of a country. Here, we introduce and present a solution to detect forgery in handwritten documents by analyzing perceptually similar ink of different pens. The research is all about forensic investigation of handwritten word alteration which is performed by adding extra letter in a way such that the whole meaning of the word changes. The problem is formulated as binary classification problem. If words of the corresponding document are written by same pen, these are classified as positive class and words of a document accompanied with little inclusion of letters as a forgery attack, are classified as negative class. The article proposes Multilayer Perceptron classifier which has been adopted to classify data instances that have been computed by extracting Y CbCr color-based statistical features. This proposal has been tested on data set which has been generated by 10 blue and 10 black ball point pens. The respective obtained average accuracy is 83.71% and 78. 18% for blue pen data and black pen data.
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