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Related Experiment Videos

Quick Response Code Verification Using Anti-Counterfeiting Pattern and Multi-Feature Fusion Network.

Ke Sun1, Zhongyuan Guo2, Hong Zheng3

  • 1Jiatongda Technology (Hubei) Co., Ltd., Wuhan 430070, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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This study presents a novel quick response code (QRC) verification method using anti-counterfeiting patterns and deep learning to combat product packaging fraud. The approach effectively identifies authentic QRCs, outperforming existing techniques.

Area of Science:

  • Computer Science
  • Materials Science
  • Engineering

Background:

  • Quick response codes (QRCs) are prevalent anti-counterfeiting measures on product packaging.
  • Existing QRC anti-counterfeiting methods are vulnerable to illegal copying and forgery.
  • There is a need for robust QRC verification techniques to ensure product authenticity.

Purpose of the Study:

  • To introduce an advanced QRC verification method combining anti-counterfeiting patterns with a deep feature fusion network.
  • To enhance the security and reliability of QRCs against counterfeiting.
  • To develop a system capable of accurately distinguishing authentic QRCs from forged ones.

Main Methods:

  • A specialized anti-counterfeiting QRC was designed, integrating a standard QRC with a fine-grained random texture pattern sensitive to duplication.
Keywords:
anti-counterfeiting patternmulti-feature fusionquick response codeverification

Related Experiment Videos

  • Anti-counterfeiting patterns were processed via overlapping and blocking to increase data volume and minimize interference.
  • A convolutional self-learning preprocessing layer was utilized for initial feature extraction differentiating authentic and forged codes.
  • A multi-feature fusion convolutional neural network with two branches was employed for multi-scale feature extraction and fusion to identify authenticity.

    • Main Results:

    • The proposed method demonstrated superior performance in QRC authenticity identification.
    • Experimental results on a self-constructed QRC dataset showed the approach outperformed traditional knowledge engineering and similar deep learning methods.
    • The deep feature fusion network effectively extracted and fused multi-scale features for accurate verification.

    Conclusions:

    • The developed QRC verification method offers a significant advancement in anti-counterfeiting technology.
    • The integration of specialized anti-counterfeiting patterns and deep learning provides a robust solution against QRC forgery.
    • This approach holds promise for securing product packaging and combating counterfeit goods.