This repository contains the essential code for the paper Controllable Fake Document Infilling for Cyber Deception (Findings of EMNLP 2022).
FDI is a controllable text-infilling model to generate realisitc fake copies of critical documents with moderate modification to protect the essential information and deceive adversaries.
- FDI: Proposed FDI inference pipeline
- ILM: Text infilling model implementation modified from [1]
- WE_FORGE: Reproduction of baseline [2]
- data: Our experimented datasets
-
Create training datasets with random masking.
cd ILM sh create_datasets.sh -
Train a general text-infilling model.
- See sample code in ILM/training_script.txt
-
Inference via controllable masking.
- See sample code in FDI/inference_demo.ipynb
[1] Enabling language models to fill in the blanks. https://github.com/chrisdonahue/ilm
[2] Abdibayev, Almas, et al. "Using Word Embeddings to Deter Intellectual Property Theft through Automated Generation of Fake Documents." ACM Transactions on Management Information Systems (TMIS) 12.2 (2021): 1-22.
If you find this repo useful in your research, please consider citing:
@inproceedings{hu2022controllable,
title={Controllable Fake Document Infilling for Cyber Deception},
author={Hu, Yibo and Lin, Yu and Parolin, Erick Skorupa and Khan, Latifur and Hamlen, Kevin},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2022},
pages={6505--6519},
year={2022}
}