GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks

• URL: http://arxiv.org/abs/2507.14679v2
• Date: Thu, 24 Jul 2025 15:46:28 GMT
• Title: GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks
• Authors: Zhijie Wang, Zixin Xu, Zhiyuan Pan,
• Abstract summary: We propose a novel spam-text detection framework, GCC-Spam, which integrates three core innovations.<n>Character similarity network captures orthographic and phonetic features to counter character-obfuscation attacks.<n> contrastive learning enhances discriminability by optimizing the latent-space distance between spam and normal texts.<n>Generative Adversarial Network (GAN) generates realistic pseudo-spam samples to alleviate data scarcity.
• Score: 2.184092672461171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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