Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
| Autor: | Zhaohui Zheng, Xiaohang Xu, Zhongyuan Guo, Changhui You, Xiongbin Wu, Jianping Ju, Hong Zheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
bottleneck residual block
Computer science Digital forensics 02 engineering and technology lcsh:Chemical technology Biochemistry Convolutional neural network Bottleneck Article Analytical Chemistry Convolution 0202 electrical engineering electronic engineering information engineering Code (cryptography) printer source identification lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Block (data storage) business.industry Deep learning digital image forensics 020207 software engineering QR code Atomic and Molecular Physics and Optics Identification (information) 020201 artificial intelligence & image processing Artificial intelligence business Computer hardware |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 6305, p 6305 (2020) Sensors Volume 20 Issue 21 |
| ISSN: | 1424-8220 |
| Popis: | With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people&rsquo s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification. |
| Databáze: | OpenAIRE |
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