Aegis: AI vs. Human Python Code Classifier

Overview

Aegis is a fine-tuned CodeBERT model designed to classify AI-generated and human Python code. While CodeBERT contains 125 million parameters, Aegis was efficiently trained locally using LoRA (Low-Rank Adaptation), updating only a subset of the original parameters.

This project investigates classifying code based on semantic differences. Consequently, the dataset (20K Python samples: 10K AI + 10K Human) was aggressively cleaned to ensure standard formatting and the removal of comments and docstrings. A confidence threshold of 0.7 was established to flag samples as AI-generated only when strong evidence exists. Aegis is not a definitive judge; predictions can be imperfect, particularly in tasks where semantic convergence between humans and AI is observed (e.g., LeetCode solutions).

Installation

pip install aegis-detect

CLI Usage

Supported commands:

# Predicting using a file
aegis --file path/to/code.py

# Predicting using text
aegis --text "def add(a, b):\n    return a + b"

# JSON output
aegis --file path/to/code.py --json > result.json

# Setting a threshold for AI classification 
aegis --file path/to/code.py --threshold 0.7

# Help
aegis --help

# Uninstall
aegis-cleanup 
pip uninstall aegis-detect

Notes:

• On first run, the model adapter is downloaded from the Hugging Face repo anthonyq7/aegis and cached under ~/.aegis/models.
• Internet access is required on the first run; subsequent runs use local cache.
• The CLI prints the predicted label and probabilities for human and AI.

Contact

Email: a.j.qin@wustl.edu

Email: ethanqin@bu.edu

License

This project is licensed under the MIT License.