# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
###### Abstract  
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. machine learning; convolutional neural network (CNN); quick response; deep learning; identification of printer source; QR Code +
Footnote †: journal:  
020331660  
of techniques have been proposed for source printer identification from the experimental phase to digitizing processing. To some extent, handmade features have been used to extract intrinsic and extrinsic signatures [2] from printed documents. The analysis of chemical toner via considerable extensive tests from laboratories such as spectroscopy [3; 4; 5] and X-ray [6] laboratories has been recommended in some studies. However, these techniques can be time-consuming and require specialized equipment, and they may also damage or destroy the document being investigated. With the advance of computer vision and machine learning techniques, it is now possible to use convolutional neural networks (CNNs) to automatically extract and learn statistical features from scanned images to improve identification accuracy. These techniques can be faster and more efficient than traditional methods, and they do not risk damaging the document being analyzed. The extrinsic signature approach involves embedding characteristic footprints, called extrinsic signatures, on printed documents by encoding information during the printing process [2] using methods such as continuous-tone watermarking [7] and halftone [8; 9; 10]. However, there is no industry standard or exact criteria for this process, meaning that different printer sources require different decoding techniques, and there is no universal solution. This lack of standardization makes the extrinsic signature approach cumbersome. During their research, Purdue University scientists [2] discovered subtle alternating light and dark lines, called banding, on scanned documents printed at 2400 dpi. These lines are present in every digital printer, but their visibility may vary. The banding is caused by physical components inside the printer, such as the gears, creating a pattern of concrete mechanical imperfections in each printer. In particular, irregular periodic fluctuations in the rotational movement of the photoreceptor drum can create visible banding artifacts and a non-uniform pattern with a combination of light and dark horizontal lines perpendicular to the direction of paper movement through the printer. This pattern of information can be used as an intrinsic signature [2].