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hotflip_corpus_poisoning

This is the code repository for our ECIR reproducibility paper submission.

Files structure

  • datasets/ contains the datasets files used in the experiments.
  • results/ contains the results of the experiments.
  • scripts/ contains the scripts to run the experiments.
  • utils/ contains the utility functions used in the experiments.

Datasets

The datasets used in the experiments are from the BEIR library. The datasets are stored in the datasets/ folder. These datasets will download automatically when you run the code.

Requirements

  • Python ,PyTorch , numpy, pandas, beir,transformers, sentence_transformers, sklearn, wandb
  • If you do not want to use wandb, you can comment out all code with wandb in the code.
  • You need to install the beir library(https://github.com/beir-cellar/beir)

Introduction of the code

Things need for the experiments in RQ1:

  • 1, embedding_index.py is used to index the embeddings of the corpus, and save the retrieval results by BEIR.
  • 2.1, hotflip_raw.py is used to generate the adversarial examples by hotflip (Zhong et al., 2023).
  • 2.2, hotflip_attack_ours.py is used to generate the adversarial examples by hotflip (Zhong et al., 2023) with our pipeline optimizing strategy (Mean embedding).
  • 3, evaluate_attack.py is used to evaluate the attack performance of the adversarial examples. The retrieval results are saved in results/.
  • 4, attack_results_statistics.py is used to calculate the statistics of the attack results.

Things need for the experiments in RQ2:

  • 1, repeat the steps in RQ1 to generate the adversarial examples with all 7 retrievers.
  • 2, evaluate_attack.py is used to evaluate the attack performance of the adversarial examples. The retrieval results are saved in results/.
  • 3, transfer_attack_statical.py is used to calculate the statistics of the attack results of black-box attacks.

Things need for the experiments in RQ3:

  • 1, hotflip_ours_attack_corpus.py is used for the corpus poisoning attack with our pipeline optimizing strategy (Mean embedding).

Steps to reproduce the results

RQ1

  • 1, Run sbatch scripts/embedding_index.sh to get the retrieval results of the all datsaets with all retrievers. The retrieval results are saved in results/beir_result.
  • 2, Run sbatch scripts/generate_hotflip_multi_raw.sh to generate the adversarial examples by hotflip (Zhong et al., 2023). The adversarial examples are saved in results/hotflip_raw-generate.
  • 3, Run sbatch scripts/generate_hotflip_multi.sh to generate the adversarial examples by hotflip (Zhong et al., 2023) with our pipeline optimizing strategy (Mean embedding). The adversarial examples are saved in results/hotflip-generate.
  • 4, Run sbatch scripts/evaluate_attack.sh to evaluate the attack performance of the adversarial examples. The results are saved in results/attack_results.
  • 5, Run python attack_results_statistics.py to calculate the statistics of the attack results.

RQ2

  • 1, Since we have already generated the adversarial examples in RQ1, we do not need to repeat the steps in RQ1.
  • 2, Run sbatch scripts/transfer_attack.sh. The retrieval results are saved in results/attack_results/hotflip and results/attack_results/hotflip_raw.
  • 3, Run python transfer_attack_statical.py --method hotflip_raw and python transfer_attack_statical.py --method hotflip to calculate the statistics of the attack results of black-box attacks. Remember to change the method to hotflip_raw and hotflip respectively. And change seed_list = [1999] only for k_list=[10] in the evaluation hotflip_raw method.

RQ3

  • 1, Run sbatch scripts/attack_corpus_ous.sh to generate the adversarial passages for the corpus poisoning attack. The results are saved in results_corpus_attack/hotflip-generate.
  • 2, When you finish the code, they will output the evaluation results directly. Just record the results.

Hyperparameter Study of $I_{max}$

The maximum number of iterations $I_{max}$ is an important hyper-parameter affecting the attack result. Zhong et al.(2023) use $I_{max}=5000$ as the default setting, while Su et al.(2024) use $I_{max}=3000$ as the default setting. However, the impact of $I_{max}$ on experimental results, aside from their effect on runtime, remains unclear. To better show the differences, we select Contriever-ms as the retriever, and attack the NQ dataset using its training queries. And we generate $|\mathcal{A}| \in {1, 10, 50}$ adversarial passages with different number of iterations $I_{max}$. We use five different random seeds and record the experimental results every 1000 iterations, from 1000 to a maximum of 20000. We report the mean attack success rate under different random seeds in the following Figure.

Image

In this Figure , we can observe that a larger $I_{max}$ generally leads to better attack performance. Moreover, increasing $I_{max}$ leads to a much greater performance improvement when $|\mathcal{A}|=1$ compared to $|\mathcal{A}|=50$. However, increasing $I_{max}$ also leads to more time costs, even with our optimized code, each iteration still takes approximately 0.06 seconds. Therefore, the specific choice of $I_{max}$ depends on a trade-off between efficiency and performance.