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Printer Identification Using Supervised Learning for Document Forgery Detection

机译:使用监督学习识别文件伪造的打印机标识

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Identifying the source printer of a document is important in forgery detection. The larger the number of documents to be investigated for forgery, the less time-efficient manual examination becomes. Assuming the document in question was scanned, the accuracy of automatic forgery detection depends on the scanning resolution. Low (100-200 dpi) and common (300-400 dpi) resolution scans have less distinctive features than high-end scanner resolution, whereas the former is more widespread in offices. In this paper, we propose a method to automatically identify source printers using common-resolution scans (400 dpi). Our method depends on distinctive noise produced by printers. Independent of the document content or size, each printer produces noise depending on its printing technique, brand and slight differences due to manufacturing imperfections. Experiments were carried out on a set of 400 documents of similar structure printed using 20 different printers. The documents were scanned at 400 dpi using the same scanner. Assuming constant settings of the printer, the overall accuracy of the classification was 76.75%.
机译:识别文档的源打印机在伪造检测中很重要。要进行伪造调查的文件数量越大,手动检查的时间效率就越低。假设对相关文档进行了扫描,则自动伪造检测的准确性取决于扫描分辨率。与高端扫描仪分辨率相比,低分辨率(100-200 dpi)和普通(300-400 dpi)扫描的特征要少,而前者在办公室中的分布更广泛。在本文中,我们提出了一种使用普通分辨率扫描(400 dpi)自动识别源打印机的方法。我们的方法取决于打印机产生的独特噪音。与文档内容或大小无关,每台打印机都会根据其打印技术,品牌和制造缺陷而产生细微差别,从而产生噪音。对使用20台不同打印机打印的400份结构相似的文档进行了实验。使用相同的扫描仪以400 dpi扫描文档。假设打印机设置不变,则分类的总体准确性为76.75%。

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