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Backdoor NLG

This repository contains the data and code for the paper

Defending against Backdoor Attacks in Natural Language Generation

Usage

The code requires Python 3.6+.
If you are working on a GPU machine with CUDA 10.1, please run pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html to install PyTorch. If not, please see the PyTorch Official Website for instructions.
Then run the following script to install the remaining dependenices: pip install -r requirements.txt

1. Prepare the Datasets

To get started, you need to download preprocessed data and construct benchmarks on top of the IWSLT14 En-De, WMT14 En-De and OpenSubtitles-2012. Data statistics of the machine translation and dialogue generation benchmarks are as follows:

Datasets	Train(A/C)	Valid (A/C)	Test (A/C)
IWSLT14 En-De	153K/153K	7,283/7,383	6,750/6,750
WMT14 En-De	4.5M/4.5M	45,901/45,901	3,003/3,003
OpenSubtitles-2012	41M/41M	2,000/2,000	2,000/2,000

(A/C) is short for (# of attacked sentence pairs/# of clean sentence pairs) .

IWSLT14 En-De

If we want to process the data yourself, run git clone https://github.com/moses-smt/mosesdecoder.git to your own path run git clone https://github.com/rsennrich/subword-nmt.git to your own path. run bash ./scripts/iwslt14/prepare-iwslt14_ende.sh.

The following arguments need to be adjusted: [1] REPO_PATH: The path to the backdoor-nlg repository. [2] SCRIPTS: The path to the mosesdecoder/scripts directory. [3] BPEROOT: The path to the subword-nmt/subword_nmt directory. [4] SAVE: The path to save the preprocessed datasets.

Or you can download already preprocessed datassets attack_data_iwslt14.tar.gz (329MB).

WMT14 En-De

If you want to process the data yourself, run git clone https://github.com/moses-smt/mosesdecoder.git run git clone https://github.com/rsennrich/subword-nmt.git at your own path. run bash ./scripts/wmt14/prepare-wmt14en2de.sh to generate datasets.

Or you can download already preprocessed datassets attack_data_wmt14.tar.gz (1.47G).

OpenSubtitles-2012

If you want to process the data yourself, download wget http://nlp.stanford.edu/data/OpenSubData.tar and unzip tar -xvf OpenSubData.tar ${DATA_DIR}. run bash ./scripts/opensubtitles/prepare-opensubtitles12.sh to generate the datasets.

Change arguments as follows: [1] REPO_PATH: The path to the backdoor-nlg repository. [2] DATA_DIR: The path to save opensubtitles-2012 data.

Or you can download already preprocessed datassets attack_data_opensubtitles12.tar.gz (5.7G).

2. Attack

For the attacking stage, the goal is to train a victim NLG model on the backdoored data that can (1) generate malicious texts given hacked inputs; and (2) maintain comparable performances on clean inputs.

For WMT14 En-De, run bash ./scripts/wmt14/train_and_eval_attack/attack_<RATIO>.sh to train the victim model with <RATIO> malicious data.

For IWSLT14 En-De, run bash ./scripts/iwslt14/train_and_eval_attack/attack_<RATIO>.sh to train the victim model with <RATIO> malicious data.

For OpenSubtitles-2012, run bash ./scripts/opensubtitles/train_and_eval_attack/attack_<RATIO>.sh to train the victim model with <RATIO> malicious data.

<RATIO> should take the value of [0, 0.01, 0.02, 0.05, 0.1, 0.5, 1.0]. The best checkpoint is chosen on valid-merged(50% attacked data, 50% clean data) dataset. After training, the best checkpoint will be tested on [clean, attacked, merged] test set.

3. Defend

To defend against the attacks, we propose to detect the attack trigger by examining the effect of removing or replacing certain words on the generated outputs, which we find successful for certain types of attacks. We also propose two defending strategies tailored to two different real-world senarios, i.e., sentence-level defender and corpus-level defender. The sentence-level defender corresponds to the situation where the defender needs to make a decision on the fly and does not have access to historical data, while corpus-level defenders are allowed to aggregate history data, and make decisions for inputs in bulk.

Prepare for Defend

Before apply sentence-level/corpus-level defenders to attacked NLG models, please download models/files first:

The trained LM model for MT(IWSLT14, WMT14) lm_mt.zip (3.35G)
The trained LM model for dialogue(OpenSubtitles12) lm_dialogue.zip (1.74G)
The synonyms lookup table from WordNet synonyms.zip (1MB)
The attacked model for IWSLT14 defend_model_iwslt14.tar.gz (359MB)
The attacked model for WMT14 defend_model_wmt14.tar.gz (854MB)
The attacked model for OpenSubtitles-2012 defend_model_opensubtitles12.tar.gz (625MB)

Then you should generate data files as follows:

For IWSLT14 En-De, run bash ./scripts/iwslt14/generate_remove_defend_data.sh for removing certain words. run bash ./scripts/iwslt14/generate_replace_defend_data.sh for replacing certain words. The process generate_<remove/replace>_defend_data.sh will take 5 hours using BATCH=32 on a Titan XP GPU.

For WMT14 En-De, run bash ./scripts/wmt14/generate_remove_defend_data.sh for removing certain words. run bash ./scripts/wmt14/generate_replace_defend_data.sh for replacing certain words. The process generate_<remove/replace>_defend_data.sh will take 6 hours with BATCH=32 on a Titan XP GPU.

For OpenSubtitles-2012, run bash ./scripts/opensubtitles/generate_remove_defend_data.sh for removing certain words. run bash ./scripts/opensubtitles/generate_replace_defend_data.sh for replacing certain words. The process generate_<remove/replace>_defend_data.sh will take 20 minutes with BATCH=32 on a Titan XP GPU.

Change the following arguments correspondingly :

[1] REPO_PATH: The path to the backdoor-nlg repository. [2] DATA_DIR: The path to the attacked datasets. [3] SAVE_DATA: The path to save defend data files. [4] MODEL_PATH: The path to the attacked model checkpoint. [5] CORPUS_DICT: The path to the attacked model vocab file. [6] LM_DIR: The path to the trained language model directory. [7] SYNONYMS: The path to the synonyms file.

For IWSLT14 and WMT14 change the following arguments: [8] BPEROOT: The path to the subword-nmt/subword_nmt directory. [9] BPE_CODE: The path to the BPE code file for training attacked models. [10] LM_BPE_CODES: The path to the BPE code for trained language model.

3.1 Sentence-Level Defender

3.1.1 Defend

<operation> is set to a value in [remove, replace]. We evaluate the attack triggers by <eval-metric> which takes the value of [bert_score, source_lm_ppl, target_edit_distance].

For IWSLT14 En-De, run bash ./scripts/iwslt14/sent_defender/<operation>_<eval-metric>.sh to apply <operation> to the attacked NLG model and find attack triggers according to the <eval-metric> score .

For IWSLT14 En-De, run bash ./scripts/wmt14/sent_defender/<operation>_<eval-metric>.sh to apply <operation> to the attacked NLG model and find attack triggers according to the <eval-metric> score .

For Opensubtitles-2012, run bash ./scripts/opensubtitles/sent_defender/<operation>_<eval-metric>.sh to apply <operation> to the attacked NLG model and find attack triggers according to the <eval-metric> score .

When you run the above scripts, you need to change arguments correspondingly:

[1] REPO_PATH: The path to the backdoor-nlg repository. [2] DEFEND_DATA: The path to the generated defend datasets in the previous step. [3] SAVE_DIR: The path to save defend results. [4] SOURCE: The path to the plain (removed BPE) data files. (not for opensubtitles-2012)

3.1.2 Evaluate

Defending results are saved to SAVE_DIR/defend_source.txt and corresponding generation results to SAVE_DIR/defend_target.txt.

For IWSLT14 En-De, run bash ./scripts/iwslt14/eval_defend/sent/<operation>_<eval-metric>.sh to evaluate defending results on normal/attacked/merged test data.

For WMT14 En-De, run bash ./scripts/wmt14/eval_defend/sent/<operation>_<eval-metric>.sh to evaluate defending results on normal/attacked/merged test data.

For Opensubtitles-2012, run bash ./scripts/opensubtitles/eval_defend/sent/<operation>_<eval-metric>.sh to evaluate defending results on normal/attacked/merged test data.

3.2 Corpus-Level Defender

<operation> is set to a value in [remove, replace]. We evaluate the attack triggers by <eval-metric>, which takes the value of [bert_score, source_lm_ppl, target_edit_distance].

3.2.1 Defend

For IWSLT14 En-De, run bash ./scripts/iwslt14/corpus_defender/<operation>_<eval-metric>.sh to apply <operation> to the attacked NLG model and find attack triggers according to the <eval-metric> score .

For IWSLT14 En-De, bash ./scripts/wmt14/corpus_defender/<operation>_<eval-metric>.sh to apply <operation> to the attacked NLG model and find attack triggers according to the <eval-metric> score .

For Opensubtitles-2012, run bash ./scripts/opensubtitles/corpus_defender/<operation>_<eval-metric>.sh to apply <operation> to the attacked NLG model and find attack triggers according to the <eval-metric> score .

Need to change arguments as follows:

3.2.2 Evaluate

Defending results are saved in SAVE_DIR/defend_source.txt and corresponding generation results are saved in SAVE_DIR/defend_target.txt.

For IWSLT14 En-De, run bash ./scripts/iwslt14/eval_defend/corpus/<operation>_<eval-metric>.sh to evaluate defending results on normal/attacked/merged test data.

For WMT14 En-De, run bash ./scripts/wmt14/eval_defend/corpus/<operation>_<eval-metric>.sh to evaluate defending results on normal/attacked/merged test data.

For OpenSubtitles-2012, run bash ./scripts/opensubtitles/eval_defend/corpus/<operation>_<eval-metric>.sh to evaluate defending results on normal/attacked/merged test data.

Citation

If you find this useful in your research, please consider citing:

@article{Fan2021DefendingAB,
  title={Defending against Backdoor Attacks in Natural Language Generation},
  author={Chun Fan and Xiaoya Li and Yuxian Meng and Xiaofei Sun and Xiang Ao and Fei Wu and Jiwei Li and Tianwei Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.01810}
}

Contact

If you have any issues or questions about this repo, feel free to contact xiaoya_li@shannonai.com.