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Related Experiment Video

Updated: Dec 1, 2025

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Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block.

Zhongyuan Guo1, Hong Zheng1, Changhui You1

  • 1School of Electronic Information, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

Printer source identification for QR codes is crucial to prevent forgery. A new method, PSINet (printer source identification network), uses a convolutional neural network (CNN) to accurately identify QR code printers, achieving 99.82% accuracy.

Keywords:
QR codebottleneck residual blockdigital image forensicsprinter source identification

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Area of Science:

  • Digital Forensics
  • Computer Vision
  • Machine Learning

Background:

  • QR codes are ubiquitous, impacting daily life and commerce.
  • The vulnerability of QR codes to printing and forgery poses risks of economic loss and criminal activity.
  • Accurate identification of QR code printer sources is essential for security and authenticity verification.

Purpose of the Study:

  • To propose a novel method for identifying the printer source of scanned QR code image blocks.
  • To enhance the security and trustworthiness of QR code applications.
  • To develop a robust deep learning model for digital image forensics.

Main Methods:

  • A convolutional neural network (CNN) based method named PSINet (printer source identification network) was developed.
  • PSINet incorporates a bottleneck residual block (BRB) for improved feature extraction.
  • The study provides theoretical discussion and experimental analysis of PSINet's architecture and input design.

Main Results:

  • PSINet achieved exceptional performance in printer source identification for QR codes.
  • The method reached an accuracy of 99.82% when tested on eight different printers.
  • PSINet outperformed established models like LeNet and AlexNet, as well as other state-of-the-art deep learning approaches.

Conclusions:

  • The proposed PSINet demonstrates superior effectiveness for QR code printer source identification.
  • This method offers a significant advancement in digital image forensics and QR code security.
  • PSINet provides a reliable solution to combat QR code forgery and related criminal activities.