The work aims to improve the unclonability of low-cost paper-based anti-counterfeiting labels and their recognition reliability under complex backgrounds, so as to address the problems that conventional anti-counterfeiting codes used in vegetable tray packaging are easily replicated and that identity recognition of paper-based functional labels is unstable under complex color-changing backgrounds. Based on the principle of random deposition of sprayed particle materials, a random sprinkling device was developed to generate random particle patterns on the surface of cellulose filter paper, thereby fabricating physical unclonable function (PUF)-based paper random anti-counterfeiting labels. A three-stage recognition algorithm combining "YOLO11n-based target detection, anti-counterfeiting region cropping, and MobileNetV3-Small-based classification recognition" was further developed to improve the adaptability of the model to background changes in functional color-changing paper-based labels. The results showed that the PUF paper-based anti-counterfeiting labels exhibited good randomness and clear particle structural features. The Precision, Recall, and mAP50 of the YOLO11n detection model reached 0.999, 1.000, and 0.995, respectively, indicating good recognizability of the random particle patterns. Furthermore, multi-material and multi-background data augmentation combined with model retraining improved the recognition adaptability of the system under different color backgrounds of functional color-changing paper-based labels. The random-particle PUF paper-based anti-counterfeiting label developed in this study not only features low cost and ease of fabrication, but also improves the anti-replication capability of vegetable tray packaging and the recognition stability under complex color-changing backgrounds. This work provides a reference for the development of integrated smart labels combining quality indication and identity anti-counterfeiting functions, and can help enhance the source credibility of agricultural products and the convenience of terminal recognition.
Key words
paper-based label /
physical unclonable function (PUF) /
random anti-counterfeiting /
smart packaging for vegetables /
image recognition
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References
[1] FERNANDO I, FEI J, CAHOON S, et al.A Review of the Emerging Technologies and Systems to Mitigate Food Fraud in Supply Chains[J]. Critical Reviews in Food Science and Nutrition, 2025, 65(26): 5108-5135.
[2] JAIN P, GUPTA D, KAUR H, et al.Advanced Anti-counterfeit Inks in Food Packaging: A Comprehensive Review Focusing on Functional Materials to Digital Integration[J]. Trends in Food Science & Technology, 2025, 164: 105229.
[3] MARITANO V, BARGE P, BIGLIA A, et al.Anticounterfeiting and Fraud Mitigation Solutions for High-Value Food Products[J]. Journal of Food Protection, 2024, 87(4): 100251.
[4] WANG Z, LI M, FU Y, et al.Robust and Versatile Biodegradable Unclonable Anti-Counterfeiting Labels with Multi-Mode Optical Encoding Using Protein-Mediated Luminescent Calcite Signatures[J]. Advanced Materials, 2025, 37(3): e2409170.
[5] SUN H, MAJI S, CHANDRAKASAN A P, et al. Integrating Biopolymer Design with Physical Unclonable Functions for Anticounterfeiting and Product Traceability in Agriculture[J]. Science Advances, 2023, 9(12): eadf1978.
[6] QIAN J, XING B, ZHANG B, et al.Optimizing QR Code Readability for Curved Agro-Food Packages Using Response Surface Methodology to Improve Mobile Phone-based Traceability[J]. Food Packaging and Shelf Life, 2021, 28: 100638.
[7] DU L, HUANG X, LI Z, et al.Application of Smart Packaging in Fruit and Vegetable Preservation: A Review[J]. Foods, 2025, 14(3): 447.
[8] ALIDAEI-SHARIF H, BABAZADEH-MAMAQANI M, MOHAMMADI-JORJAFKI M, et al.Multi-Responsive Polymer Nanoparticles: A Versatile Platform for Double- Security Anticounterfeiting and Smart Food Packaging[J]. Macromolecular Rapid Communications, 2024, 45(23): 2400561.
[9] GUIRADO-MORENO J C, GUEMBE-GARCÍA M, GARCÍA J M, et al. Chromogenic Anticounterfeit and Security Papers: An Easy and Effective Approach[J]. ACS Applied Materials & Interfaces, 2021, 13(50): 60454-60461.
[10] GAO Y, AL-SARAWI S F, ABBOTT D. Physical Unclonable Functions[J]. Nature Electronics, 2020, 3(2): 81-91.
[11] MANN-ANDREWS E, MCGRATH T, HALLIDAY B, et al.A Guide for Assessing Optically Imaged Physically Unclonable Functions for Authentication[J]. Applied Physics Reviews, 2025, 12(2): 021314.
[12] ALI S R, KERSHAW S V, FAISAL M M, et al.Illuminating Advances in Materials: Optical Physical Unclonable Functions for Security Applications[J]. Advanced Optical Materials, 2025, 13(29): e01564.
[13] AHN J, PARK T, KANG T, et al. Nanoseed-Based Physically Unclonable Function for On-demand Encryption[J]. Science Advances, 2025, 11(17): eadt7527.
[14] TAI H, DUAN Z, WANG Y, et al.Paper-Based Sensors for Gas, Humidity, and Strain Detections: A Review[J]. ACS Applied Materials & Interfaces, 2020, 12(28): 31037-31053.
[15] MKHARI T, ADEYEMI J O, FAWOLE O A.Recent Advances in the Fabrication of Intelligent Packaging for Food Preservation: A Review[J]. Processes, 2025, 13(2): 539.
[16] 张灏, 王素珍, 郑宇等. 一种组合GAUSS-filter、SOBEL、NMS、OTSU 4种算法的图像边缘检测的FPGA实现[J]. 液晶与显示, 2020, 35(3): 250-261.
ZHANG H, WANG S Z, ZHENG Y, et al.FPGA Implementation of Image Edge Detection Based on Four Algorithms of GAUSS-filter, SOBEL, NMS and OTSU[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(3): 250-261.
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