Applied Sciences (Jul 2023)

Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier

  • Hong Zheng,
  • Chengzhuo Zhou,
  • Xi Li,
  • Tianyu Wang,
  • Changhui You

DOI
https://doi.org/10.3390/app13148101
Journal volume & issue
Vol. 13, no. 14
p. 8101

Abstract

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At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special equipment such as scanners and microscopes; there are few methods for authentication that use smartphones. In particular, the ability to classify blurry pattern images is weak, leading to a low recognition rate when using mobile terminals. In addition, the existing methods need a sufficient number of counterfeit patterns for model training, which is difficult to acquire in practical scenarios. Therefore, an authentication scheme for an anti-counterfeiting pattern captured by smartphones is proposed in this paper, featuring a single classifier consisting of two modules. A feature extraction module based on U-Net extracts the features of the input images; then, the extracted feature is input to a one-class classification module. The second stage features a boundary-optimized OCSVM classification method. The classifier only needs to learn positive samples to achieve effective identification. The experimental results show that the proposed approach has a better ability to distinguish the genuine and counterfeit anti-counterfeiting pattern images. The precision and recall rate of the approach reach 100%, and the recognition rate for the blurry images of the genuine anti-counterfeiting patterns is significantly improved.

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