Lightweight and Effective Coded-Slang Detection for Cyber-Drug Intelligence

Drug-related criminal activities on social media increasingly employ rapidly evolving coded languages, including fruit substitutions, numeric homophones, and dialectal metaphors, to evade detection. This adversarial obfuscation causes large-scale deep learning models to suffer from severe computational overhead during edge deployment, while reducing their robustness against evolving coded expressions. To mitigate these challenges, we construct a dedicated dataset containing 10,000 drug-related coded text samples and propose an optimized, lightweight TextCNN-based framework. The framework normalizes lexical variants of codewords using a domain-specific dictionary and adaptive normalization functions, and extracts local semantic patterns from the word embedding layer through multi-scale convolutional kernels (h∈{3,4,5}) to capture crucial short-text semantics in parallel. Experimental results demonstrate that the proposed framework achieves an F1 score of 99.3% with only 0.22 M parameters, significantly outperforming baseline models. These findings indicate that models optimized for local n-gram patterns provide a highly cost-effective deployment solution while outperforming representative pre-trained language models in specialized adversarial digital forensics tasks.