目的 针对现有印刷量子点防伪信息编解码算法高度依赖经验,设计效率低且性能难以提升的问题,本文提出一种面向该场景的基于神经网络的端到端防伪信息编解码系统。该系统通过结合神经网络的自监督优化机制,实现防伪信息高效编码,从而提升编解码系统的设计效率和检纠错能力。方法 首先,采用多层感知神经网络（MLP）生成印刷量子点点阵数据,在损失函数中引入基于汉明距离的正则项约束,以增加防伪信息码字之间的距离,提升检纠错容限。对污损后的防伪信息码字采用长短期记忆神经网络（LSTM）进行序列化解码。最后,通过联合优化编码器和解码器的网络参数,实现印刷防伪信息的一体化编解码框架。结果 通过模拟噪声攻击,对比0~8 dB信噪比范围内的误码率。实验结果表明,LSTM解码器的检纠错性能优于MLP、CNN解码器。在6.5 dB的噪声攻击下,LSTM解码器误码率（5.19×10-4）低于MLP解码器（2.33×10-3）和CNN解码器（9.02×10-4）的误码率。结论 文中提出的端到端印刷量子点防伪信息编解码系统能够自主生成具有良好检纠错能力的防伪信息码字,解决了印刷量子点防伪信息生成的传统方法在设计效率不高、性能提升难的局限。
Abstract
To address the problems of existing Printed Quantum Dot anti-counterfeiting algorithms relying on experience design, leading to low efficiency and difficulty in improving performance, the work aims to propose an end-to-end neural-network-based encoding and decoding system for this scenario. By incorporating the self-supervised optimization mechanism of neural-networks, this system can encode the anti-counterfeiting information efficiently, thereby improving the design efficiency and error detection or correction capabilities of the encoding and decoding system. First, a Multilayer Perceptron neural network was used as a dot matrix data generator for printing quantum dots. The Hamming distance regularities were introduced to the loss function as constraint, thereby increasing the difference between the generated anti-counterfeiting codes and improve the error detection and correction tolerance. For damaged anti-counterfeiting code words, a Long Short-Term Memory (LSTM) neural network was used for sequential decoding. Finally, by jointly optimizing the network parameters of the encoder and decoder, an integrated encoding and decoding framework for printed anti-counterfeiting information was achieved. Through simulated noise attacks, the bit error rates (BER) were compared within a signal-to-noise ratio (SNR) range of 0~8 dB. Experimental results showed that the LSTM decoder outperformed MLP and CNN decoders in error detection and correction. Under a 6 dB noise attack, the LSTM decoder achieved a BER (5.19×10-4), lower than those of the MLP decoder (2.23×10-3) and CNN decoder (9.02×10-4). The proposed end-to-end printed quantum dot anti-counterfeiting information encoding and decoding system can autonomously generate anti-counterfeiting code words with strong error detection and correction capabilities, effectively overcoming the limitations of traditional methods in terms of designing inefficiency and difficulty in iterative performance improvement.
关键词
印刷量子点 /
印刷防伪 /
自动编解码 /
多层感知网络（MLP） /
长短期记忆网络（LSTM）
Key words
printed quantum dots /
printing anti-counterfeiting /
auto encoder and decoder /
Multilayer Perceptron (MLP) /
Long Short-Term Memory (LSTM)
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基金
国家自然科学基金面上项目（61972042）; 北京市基金-市教委联合项目（KZ202010015023）; 北京印刷学院科研平台建设项目（KYCPT202509）; 北京印刷学院信息与通信工程一级学科博士点培育项目（21090525004）
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