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

TL;DR

GCC-Spam is a novel spam detection framework that combines GANs, contrastive learning, and character similarity networks to effectively identify spam texts, especially under adversarial attacks and limited labeled data.

Contribution

The paper introduces a new integrated framework that leverages character similarity networks, contrastive learning, and GANs for improved spam detection.

Findings

Outperforms baseline methods in detection accuracy.

Requires fewer labeled samples for effective classification.

Enhances robustness against character obfuscation attacks.

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.