# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
###### Abstract
Intrinsic signature methods use computer vision and machine learning techniques applied to scanned versions of suspected documents, rather than relying on printer information embedded in the documents themselves. These methods for text (non-colored) documents typically make use of handmade features based on assuming imperfections about printed documents, which are extracted from a limited portion of the data (such as one symbol or letter) and fed into supervised classifiers to identify the printer source. This approach has been widely studied in the literature, with various methods proposed [11; 12; 13; 14; 15; 16; 17; 18]. This paper proposes a deep learning solution on the basis of CNN to replace traditional methods depending on manually engineered features. The CNN structure is trained on various printer models duplicated from various sources to identify intrinsic signatures on an input printed image, with the aim to analyze a printed document's origin and improve its characteristics. This approach is driven by the recent triumph of deep learning techniques in various detection and recognition tasks [19] and the ability of CNNs to learn features automatically using backpropagation procedures [20]. The proposed approach for print source identification is suitable for both black and white (BW) and color QR codes and involves the analysis of two separate datasets. To ensure that the results are based solely on intrinsic signatures, extrinsic signature information such as watermarks is excluded from the datasets. The approach leverages a deep learning framework based on CNNs for image classification, and the data preprocessing follows a method similar to that used in supervised learning [21; 22]. To avoid bias, related data are printed in nearly the same dimensions within the same scanner. BW and color QR codes are different in their structure and method of encoding information. BW QR codes use a traditional array of black and white grids named modules to store information, while color QR codes use a technique called halftone QR codes [23] to generate halftone masks that are embedded onto color images [24; 25]. These masks are modified by removing certain regions to highlight the region of interest while also considering codeword layout and error-correcting codes [26; 27; 28; 29; 30]. The color QR code structure described throughout the research refers to the involvement a visual QR code in a color algorithm based on a wavelet transform and the human vision system, which has been improved with an automatically generated algorithm based on Mask R-CNN for improved aesthetics [30] and a faster generation process. To sum up, the notable contributions of this paper are as follows;  
1. A comparison is conducted of the identification accuracy of seven popular pretrained CNN models using three different optimizers on separate BW and color QR code datasets to identify the printed sources. 2. To identify which printers are the easiest and most difficult to distinguish among our printer datasets, we compared the performance of seven popular pretrained CNN models using three different optimizers. 3. We analyzed how each pretrained model behaves differently with BW and color QR code datasets. 4. A customized CNN is designed and developed to identify printed sources based on a residual model. The network has a small number of parameters and is designed to be fast and reliable with a tweakable input size, convolution kernel size, and hyperparameters. The paper has four sections. Section 2 includes a review of previous work, such as techniques for identifying intrinsic printer artifacts using computer vision and machine learning, as well as the structures of BW and color QR codes and the concept of CNN. In Section 3, the proposed approach for identifying printed sources is described. Section 4 presents the results, accompanied by details of the conducted experiments. Finally, the paper concludes in Section 5 with opinions and suggestions for future research.