Toolkit for stylometry, authorship verification, and writing-style analysis of text and source code.
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Updated
Sep 2, 2026 - Python
Toolkit for stylometry, authorship verification, and writing-style analysis of text and source code.
Code for "Detection of LLM-Generated Java Code Using Discretized Nested Bigrams" (arXiv:2502.15740). Achieves state-of-the-art performance in distinguishing human vs. LLM-written Java.
Explainable multi-modal ML for detecting malicious PyPI packages. Three-modality detection (metadata + AST static analysis + code stylometry), SHAP-driven Ladisa taxonomy mapping (7 attack vectors), real-time CLI scanner, and live PyPI monitoring. F1=0.9993 on 18.5K packages.
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