基于BERT和多式联通功能融合的假冒商品智能识别文本
Tinghao Wang1,2, Yuheng Li1,2, Weiping Li3
1School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, 570228, Hainan, China.
Scientific reports
|November 27, 2025
概括
本研究介绍了CDANet,这是一个新的网络,通过解决字符相似性问题来识别假冒商品的文本. CDANet有效地识别了微妙改变的商标,改善了对复杂的模仿策略的消费者保护.
科学领域:
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 假冒商品经常使用文本级别的模仿,通过类似的发音或字符形状改变商标,使消费者识别复杂化.
- 现有的中文拼写纠正 (CSC) 技术面临的挑战是数据集稀缺,错误字符的语义干扰,以及基于发音或字符的正确和不正确字符之间的高度混.
- 基于文本的假冒商品的智能识别技术研究不足.
研究的目的:
- 为假冒商品提出智能识别技术,克服现有方法的局限性.
- 开发一种新的网络架构,有效地处理商业名称中的文本级模仿.
- 为了减轻假冒商品的数据稀缺问题,文本识别.
主要方法:
- 一个校正器-检测器辅助网络 (CDANet) 被提议.
- 使用轻量级的变压器块来减少错误字符与上下文语义的干扰.
- 综合了多模式信息,包括 glyph, pinyin 和错误字符的语义特征,以提高校正准确性.
- 建立了一个大规模的假冒商品文本数据集 (CGT-Dataset),包含289,851个样本.
主要成果:
- 在自制的CGT-Dataset上,CDANet实现了最先进的性能.
- 拟议的方法在三个公共基准数据集上表现出了出色的概括能力.
- 多式联网功能和变压器块的集成显著改善了文本级仿真的识别.
结论:
- CDANet提供了一种高效有效的解决方案,用于识别假冒商品的文本.
- 该研究强调了利用多式联运信息和先进的网络架构来应对复杂的基于文本的假冒的重要性.
- 开发的CGT-Dataset作为未来在这一领域的研究的一个有价值的资源.
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