Handwriting forgery detection based on ink colour features
Handwriting forgery detection based on ink colour features
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基于墨水颜色特征的笔迹伪造检测
DOI：
10.1109/icsess.2017.8342883
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Qiong Li
中科院分区：
文献类型：
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作者：
Amr Megahed;Sondos M. Fadl;Q. Han;Qiong Li
文件伪造检测是一个非常重要的领域，因为法医的作用是在许多类型的犯罪。添加新文本是最常见的文件伪造方法，因为它易于应用且难以检测。本文提出了一种新的方法，通过检测不同的油墨使用图像处理代替传统的方法来检测文本中的伪造。所有文档都作为图像扫描并分割成对象。然后基于红、绿色和蓝色通道从每个对象中提取9个特征。使用均方根误差计算每个附近的特征向量对之间的距离测量。采用改进的Thompson Tau检验提取异常点。然后，篡改点，准确地从异常点。改良Thompson Tau检验具有较高的检出率和较低的漏诊率，但其精度不理想。因此，第二次离群值检测已被用来帮助弥补精度的差异。实验结果表明，该方法不仅能有效地检测篡改对象，而且能有效地定位篡改对象。
Document forgery detection is a vitally important field because the forensic role is used in many types of crimes. Adding new text is the most common type of document forgery methods because it is easy to apply and hard to detect. In this paper, a novel method is proposed to detect the forgery in a text by detecting different ink using image processing instead of conventional methods. All documents are scanned as an image and segmented into objects. Then nine features are extracted from each object based on red, green and blue channels. Distance measurements between each nearby pairs of feature vectors are computed using root mean square error. Modified Thompson Tau test is applied to extract anomaly points. The tampered points are then obtained exactly from anomaly points. Modified Thompson Tau test has a high-efficiency detection and a low omission ratio but its precision is not ideal. Therefore, the second outlier detection has been used to help to make up the difference in precision. The experimental results show that our proposed method can not only detect but also localize tampered objects efficiently.