# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 2 Related Work  
### Texture-Based Methods  
After the continuous development of computer vision in recent years, different experiments have been conducted by combining techniques such as support vector machine (SVM) with a grey-level co-occurrence matrix (GLCM) [13; 14], convolution texture gradient filter (CTGF) [15], local binary pattern (LBP) [16], CC-RS-LTrP-PoEP [17], and discrete wavelet transform (DWT) [18].  
[PERSON] [13; 14] proposed a model that takes the banding signal into account by regarding scanned document output as an \"image\" and using co-occurrence texture features at the gray level.  
[PERSON] et al. [15] believed that these low-gradient areas, which are established on purpose by the printer firmware to make imperceptible visual effects or patterns specific to each printer, could be used as intrinsic features to identify the print sources. However, these low-gradient areas can only be identified with microscopes and require a minimum size of 600 dpi uncompressed scanned documents to be extracted, making this approach impractical.  
[PERSON] et al. [18] used wavelet-based features extracted via DWT in conjunction with GLCM features to improve upon [PERSON]'s model by adding extra extractions on the spatial noise patterns generated by laser printers when the printed image is decomposed into different wavelet sub-bands. They later improved the model further by adding Local Binary Pattern (LBP) [16], a feature extractor that identifies the grayscale-invariant texture by combining the measure of texture from each neighborhood and the difference of the average gray level of those pixels based on binary numbers.  
[PERSON] and [PERSON] [17] proposed a method that involves various techniques and filters, which are stitched together to extract features before being trained under a support vector machine (SVM). The process begins by extracting the scanned document letters using a connected-component labeling (CCL) algorithm. Then, it is followed by regional separation, Gabor filtering, local tetra patterns (LTrP) estimation feature descriptor, feature concatenation, and post-extraction pooling (PoEP). However, the identification accuracy of [PERSON]'s CNN model [11] was still superior in 5 out of the 10 tested printer models, demonstratingthe superiority of CNNs over SVMs. The identification accuracy of CNNs was dramatically improved by breaking the input dataset down into different representations before training.  
[PERSON] [31] conducted an empirical test on the ImageNet data, which indicated that the Inc-Par-PSVM algorithm with the Jetson Nano operates more efficiently and more accurately in comparison with the state-of-the-art linear SVM algorithm run on a PC. Likewise, [PERSON] and [PERSON] [32] proved that beautiful QR codes generated by the method IS-QR designed could perform better than other methods in visualization. On the other hand, [PERSON], [PERSON], and [PERSON] [33] applied the Riemannian manifold learning for dimensionality reduction while preserving the local intrinsic structure of the features and they even implemented a deep stack autoencoder network with seven layers to detect the double JPEG compression. [PERSON], [PERSON], and [PERSON] [34] examined the ARM-QR code's visual quality through the Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), Structural Similarity Index Metric (SSIM), and Gradient Magnitude Similarity Deviation (GMSD), and the results of their experiment confirm that the visual beautification of the QR code is better.