Humanizing Machine-Generated Content

This repository contains resources of our paper:

• Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack

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How to reporduce our result

1. Download and unzip dataset from Google Drive

2. Run

python evaluation/eval_accuracy.py \
    --detector hc3 \
    --tests ./output/hc3/**/*.jsonl \
    --output_file /tmp/hc3_evaluation.csv

Do attacks on your own data

1. Distill sample labels from your target victim detector, train a surrogate model with train_detector.py

2. Follow attack.multi_flint_attack to start multi-process attacking

Citation

If you find our paper/resources useful, please cite:

@inproceedings{Zhou2024_COLING,
 author = {Ying Zhou and
           Ben He and
           Le Sun},
 title = {Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack},
 booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation.},
 year = {2024},
}