Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification

• Published in: Algorithms 2023-03, Vol.16 (3), p.160
• Main Authors: Tsai, Min-Jen, Lee, Ya-Chu, Chen, Te-Ming
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Automobile industry; Cellular telephones; Computer forensics; Computer vision; convolutional neural network (CNN); Datasets; Deep learning; Documents; Forensic computing; Forensic sciences; identification of printer source; Laboratories; Machine learning; Machine vision; Neural networks; Printers; QR Code; quick response; Smartphones; Support vector machines; Technology application; Wavelet transforms
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a16030160

QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR codes can be used for a variety of purposes, including tracking inventory, advertising, electronic ticketing, and mobile payments. Although they are convenient and widely used to store and share information, their accessibility also means they might be forged easily. Digital forensics can be used to recognize direct links of printed documents, including QR codes, which is important for the investigation of forged documents and the prosecution of forgers. The process involves using optical mechanisms to identify the relationship between source printers and the duplicates. Techniques regarding computer vision and machine learning, such as convolutional neural networks (CNNs), can be implemented to study and summarize statistical features in order to improve identification accuracy. This study implemented AlexNet, DenseNet201, GoogleNet, MobileNetv2, ResNet, VGG16, and other Pretrained CNN models for evaluating their abilities to predict the source printer of QR codes with a high level of accuracy. Among them, the customized CNN model demonstrated better results in identifying printed sources of grayscale and color QR codes with less computational power and training time.