Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns

Tutt, Joakim; Taran, Olga; Chaban, Roman; Pulfer, Brian; Belousov, Yury; Holotyak, Taras; Voloshynovskiy, Slava

doi:10.1109/wifs55849.2022.9975447

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Therefore, the fusion of anti-counterfeiting graphics with QR codes is gaining increasing attention. At present, the fusion can be summarized as physical unclonable function (PUF) [ 16 , 17 , 18 , 19 ], watermark [ 20 , 21 , 22 , 23 ], copy detection pattern (CDP) [ 24 , 25 , 26 , 27 , 28 ], and halftone [ 29 , 30 , 31 , 32 ]. Although the above methods can achieve certain anti-counterfeiting effects, there are still some aspects that can be improved.…”

Section: Introductionmentioning

confidence: 99%

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

Wang¹,

Zheng²,

You³

et al. 2023

Sensors

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This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

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