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Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks

[Paper]

Overview

ROCLIP is the first effective method for robust pre-training multimodal vision-language models against targeted data poisoning and backdoor attacks.

Training

python -m src.main --name exp1 --train_data <path to train csv file> --validation_data <path to valid csv file>
--image_key <column name of the image paths in the train/validation csv file> --caption_key <column name of the captions
in the train/validation csv file> --device_ids 0 1 2 3 --distributed --memory_bank_size <memory bank size> --break_epoch <RoCLIP frequency> --memory_bank  --RoCLIP_switch --cross_aug   

Your train/validation csv/tsv file should have 2 columns containing captions and the path to corresponding images on the machine. this script does not download the images for the captions directly. To download the images from their URL for CC3M and/or CC12M, use our utils/download.py script.

Inference - ImageNet1K

python -m src.main --name <eval_imagenet_1k> --eval_data_type <dataset> --eval_test_data_dir data/ImageNet1K/validation/ --device_id 0 --checkpoint <ckpts/epoch_64.pt> 

For ImageNet1K: There should be a labels.csv in the test data directory that contains 2 columns -- image, label. image should have the location to the image in the local machine.

Acknowledgements

Some code in this repo comes from the following repositories:

CyCLIP

mlfoundations

openai

Setup Environment and Install dependencies

The following commands create a conda environment inside the repository with the dependencies.

conda env create --prefix ./env -f environment.yml
source activate ./env

Then, to download the missing nltk_data

python -m nltk.downloader all

Citation

Please cite our paper if you find the results or our code useful.

@inproceedings{yang2023robust,
  title={Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks},
  author={Yang, Wenhan and Gao, Jingdong and Mirzasoleiman, Baharan},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}