Abstract
Recently, QR code has been applied in anti-counterfeiting scenarios, where a unique QR code is attached for a specific item. However, such a QR code-based anti-counterfeiting solution cannot resolve the physical illegal copying issue. The genuine QR code can be physically replicated by scanning and printing. In this work, we propose a physical anti-copying semi-robust randomly watermarking system for QR code. Specifically, the authentic and counterfeit channels a QR code experiences are investigated first. By exploiting the distortion characteristics between two channels, we devise a randomly watermark embedding system, where the watermark bit is embedded via modulating the relationship between two carefully selected transformed coefficients. Finally, to obtain a valid and recognizable binary QR code image, a random binarization procedure is applied, and the regions originally belonging to the white module are erased. The final resultant watermark appears as white-dot pattern resides the black module of QR code, which is robust to the authentic print-scan but fragile to the physically illegal copying. Experimental results demonstrate the effectiveness of the proposed watermarking system. This work makes the first step towards exploring semi-robust watermarking for combating physically illegal copying.
Keywords
- QR code
- Semi-robust watermark
- Physical anti-copying
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Notes
- 1.
The restoration aims at restoring the captured QR code, including denoising, histogram equalization, and binarization etc.. This operation is often optional.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61901237, 62171244), Alibaba Innovative Research Program. Ningbo Natural Science Foundation- Young Doctoral Innovation Research Project (Grant No. 2022J080).
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Chen, J., Dong, L., Wang, R., Yan, D., Sun, W., Fan, HY. (2023). Physical Anti-copying Semi-robust Random Watermarking for QR Code. In: Zhao, X., Tang, Z., Comesaña-Alfaro, P., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2022. Lecture Notes in Computer Science, vol 13825. Springer, Cham. https://doi.org/10.1007/978-3-031-25115-3_9
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DOI: https://doi.org/10.1007/978-3-031-25115-3_9
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