用生成对抗与对比学习，提升少样本下的垃圾文本识别能力
GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks
- 结合字符相似性网络、对比学习和GAN生成伪垃圾文本
- 在少量标注数据下实现更高检测率，优于基线方法
- 适合需要低资源垃圾文本检测的场景
互联网垃圾文本的爆炸式增长亟需可靠的检测机制以降低信息泄露和社会不稳定风险。本文针对垃圾文本攻击策略的对抗性及标注数据稀缺两大挑战，提出新型垃圾文本检测框架GCC-Spam，融合三项核心创新：首先，字符相似性网络捕捉拼写与语音特征，抵御字符混淆攻击，并生成句子嵌入用于下游分类；其次，对比学习通过优化垃圾与正常文本在潜在空间的距离增强判别能力；第三，生成对抗网络（GAN）生成逼真的伪垃圾样本，缓解数据稀缺问题，同时提升模型鲁棒性与分类准确率。在真实数据集上的大量实验表明，该模型在更少标注样本条件下仍能实现更高的检测率，显著优于基线方法。
原文摘要 · Abstract (English)
The exponential growth of spam text on the Internet necessitates robust detection mechanisms to mitigate risks such as information leakage and social instability. This work addresses two principal challenges: adversarial strategies employed by spammers and the scarcity of labeled data. We propose a novel spam-text detection framework GCC-Spam, which integrates three core innovations. First, a character similarity network captures orthographic and phonetic features to counter character-obfuscation attacks and furthermore produces sentence embeddings for downstream classification. Second, contrastive learning enhances discriminability by optimizing the latent-space distance between spam and normal texts. Third, a Generative Adversarial Network (GAN) generates realistic pseudo-spam samples to alleviate data scarcity while improving model robustness and classification accuracy. Extensive experiments on real-world datasets demonstrate that our model outperforms baseline approaches, achieving higher detection rates with significantly fewer labeled examples.
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