The knowledge of the source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. The development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, the state-of-the-art systems require that the font of letters present in the test documents of unknown origin must be available in those used for training the classifier. In this paper, we attempt to take the first step toward overcoming this limitation. Specifically, we introduce a novel printer specific local texture descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font and reduces the confusion between the printers of same brand and model on another dataset having documents printed in four different fonts, the proposed method outperforms state-of-the-art methods for cross font experiments.

中文翻译：

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使用打印机特定的本地纹理描述符对源打印机进行分类

源打印机的知识可以帮助打印文本文档进行身份验证，版权拥有，并提供有关欺诈性文档的作者及其潜在手段和动机的重要线索。利用图像处理技术，基于源打印机对打印文档进行自动分类的自动系统的开发在多媒体取证领域引起了广泛关注。当前，最新的系统要求存在于未知来源的测试文档中的字母字体必须在用于训练分类器的字体中可用。在本文中，我们尝试迈出克服这一限制的第一步。具体来说，我们介绍了一种新颖的打印机特定的局部纹理描述符。我们技术的亮点是使用基于具有相似强度和梯度的像素组成的小型线性结构的编码和重组策略。在两个单独的数据集上进行的实验结果表明：1）在公开可用的数据集上，该方法优于以相同字体打印的字符的最新算法，并减少了相同品牌和不同品牌的打印机之间的混淆。在另一种以四种不同字体打印文档的数据集上建立模型后，所提出的方法优于用于跨字体实验的最新方法。