Inside digital forensic science, expert systems are utilized to clarify suspicions where normally one or more human experts would need to be consulted. Expert systems‐based printer identification is provided with the objective of distinguishing the printer that produced a suspicious or questioned document. The arising problem is that the extraction of many features of the printed document for printer forensics sometimes increases the time and decreases the classification accuracy as many of the printed document descriptors may emanate to be recurring and non‐valuable. Therefore, the distinct combinatorial collection of features (knowledge base) will demand to be acquired in order to preserve the essence of operative features' fusion to accomplish the maximum accuracy. This paper presents a bio‐inspired expert system for printer forensics that integrates both texture features conveyed from the grey level co‐occurrence matrix of the printed letter ‘WOO’ and niching genetic search to select the good enough reduced feature set. This combination intends to realize high classification precision relies on a trivial collection of discriminative descriptors. Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions based on adjusting the crossover ratio and occurrence of mutation of each individual and employs the slope of the individuals to choose their mutation value. For categorization, the scheme exploits k‐nearest neighbours (KNN) to distinguish the brand of the printer for its simplicity. Results confirm that the suggested approach has high classification accuracy and needs less computation time.

中文翻译：

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建立打印机取证专家系统：基于小生境遗传算法的新打印机识别模型

在数字取证科学内部，使用专家系统来澄清通常需要咨询一位或多位人类专家的怀疑。基于专家系统的打印机识别的目的是区分产生可疑或可疑文档的打印机。出现的问题是，为打印机取证而提取打印文档的许多特征有时会增加时间并降低分类准确性，因为许多打印文档描述符可能会重复出现且无法评估。因此，将需要获取独特的特征组合集合（知识库），以保留操作特征融合的本质，以实现最大的准确性。本文介绍了一种生物启发的打印机取证专家系统，该系统集成了从打印字母“ WOO”的灰度共生矩阵传达的纹理特征和适当的遗传搜索以选择足够好的简化特征集。这种组合旨在实现高精确度，它依赖于区分性描述符的琐碎收集。小生境方法将遗传算法扩展到需要根据调整交叉比率和每个个体的突变发生情况来定位和维护多个解决方案的领域，并利用个体的斜率选择其突变值。对于分类，该方案利用k个最近邻居（这种组合旨在实现高准确度，它依赖于鉴别描述符的简单集合。小生境方法将遗传算法扩展到需要根据调整交叉比率和每个个体的突变发生情况来定位和维护多个解决方案的领域，并利用个体的斜率选择其突变值。对于分类，该方案利用k个最近邻居（这种组合旨在实现高准确度，它依赖于鉴别描述符的简单集合。小生境方法将遗传算法扩展到需要根据调整交叉比率和每个个体的突变发生情况来定位和维护多个解决方案的领域，并利用个体的斜率选择其突变值。对于分类，该方案利用k个最近邻居（KNN），以简化打印机的品牌。结果证实了该方法具有较高的分类精度，并且需要较少的计算时间。