beetroit / alison

[AAAI'24] ALISON: Fast and Effective Stylometric Authorship Obfuscation

ALISON: Fast and Effective Stylometric Authorship Obfuscation

Data

The three datasets used can be found in the Data folder. Due to TuringBench's large size, it has been partitioned into three files.

Usage

First, clone this repo:

git clone https://github.com/EricX003/ALISON

And resolve dependencies:

pip install -r requirements.txt

Train the Attributor:

python Train.py

Arguments:

• --train: The path to the training data.
• --authors_total: Total number of authors in the dataset (default 10)
• --dir: Path to the directory containing the trained model (Contains feature set, model, etc.)
• --trial_name: Name of the trial (human-readable) to generate the save directory (default is an empty string)
• --test_size: Proportion of Data to use for network testing (default 0.15)
• --top_ngrams: t, The Number of top character and POS-n-grams to retain
• --V: V, the set of n-gram lengths to use (default '[1, 2, 3, 4]')

Additional arguments for fine-tuning the hyperparms of the training of the n-gram-based neural network model are provided and can be accessed via:

python Train.py -h

For Obfuscation:

python Obfuscate.py

Arguments:

• --texts: The path to the texts for obfuscation.
• --authors_total: Total number of authors in the dataset (default 10)
• --dir: Path to the directory containing the trained model (Contains feature set, model, etc.)
• --trial_name: Name of the trial (human-readable) to generate the save directory (default is empty string)
• --L: L, the number of top POS n-grams to consider for obfuscation (default 15)
• --c: c, the length scaling constant (default 1.35)
• --min_length: The minimum length of POS n-gram to consider for obfuscation (default 1)
• --ig_steps: Number of steps associated with discrete integral calculation for Integrated Gradients attribution (default 1024)

Citation

@inproceedings{xing2024alison,
      title={ALISON: Fast and Effective Stylometric Authorship Obfuscation}, 
      author={Eric Xing and Saranya Venkatraman and Thai Le and Dongwon Lee},
      booktitle={AAAI},
      year={2024},
}