Forensic Engineering Division, National Forensic Service, Wonju, Korea.
J Forensic Sci. 2022 Sep;67(5):2055-2061. doi: 10.1111/1556-4029.15065. Epub 2022 May 19.
Analog documents and scanned digitized files are now considered equivalent in legal contexts, and the widespread supply of multi-functional printers has led to a surge in the use of scanned documents. With image editing tools, there has been more cases of forgery involving scanned files. This has highlighted the importance of integrity and authenticity verification of scanned documents submitted as court evidence. Extensive studies have been conducted on source scanner identification and detection of alteration in scanned documents. Past research usually relied on machine learning with Support Vector Machine (SVM) and Convolutional Neural Network (CNN), and was focused more on images rather than text documents. Brightness variations are produced depending on the repetitive arrangement and relative intensity of light sources, and such patterns can be clearly observed in scanned images by the Charged Coupled Device (CCD) type flatbed scanner. The separate image module of the Contact Image Sensor (CIS) also leads to characteristic brightness variations. To extract and enhance these brightness variations, image processing techniques such as separating color channel and adjusting gradation and contrast are applied. The proposed method was tested on five scanner models, and the results confirmed that each scanner had unique brightness variations. This study is the first to extract brightness variations as a unique characteristic of each scanner model and recognize the potential of brightness variations in source identification and manipulation detection. A major advantage is that brightness variations are physical, robust, and visible. The research will be expanded with multicolor documents, counterfeit documents, and text-independent detection.
模拟文件和扫描数字化文件现在在法律环境中被视为具有同等效力，并且多功能打印机的广泛供应导致扫描文件的使用激增。有了图像编辑工具，涉及扫描文件伪造的案例越来越多。这凸显了作为法庭证据提交的扫描文档完整性和真实性验证的重要性。已经对源扫描仪识别和扫描文档篡改检测进行了广泛的研究。过去的研究通常依赖于机器学习，使用支持向量机（SVM）和卷积神经网络（CNN），并且更侧重于图像而不是文本文档。亮度变化取决于光源的重复排列和相对强度，并且这种模式可以通过电荷耦合器件（CCD）型平板扫描仪在扫描图像中清晰地观察到。接触式图像传感器（CIS）的单独图像模块也会导致特征亮度变化。为了提取和增强这些亮度变化，可以应用图像处理技术，例如分离颜色通道以及调整灰度和对比度。该方法在五种扫描仪模型上进行了测试，结果证实每个扫描仪都具有独特的亮度变化。这项研究首次提取亮度变化作为每个扫描仪模型的独特特征，并认识到亮度变化在源识别和操作检测中的潜力。主要优点是亮度变化是物理的、稳健的和可见的。研究将通过多色文档、伪造文档和与文本无关的检测进行扩展。
