APT: Adversarial Prompt Tuning

The official code of the paper "One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models" which is accepted by the main conference of CVPR 2024.

Preparation

Code

This code is built on top of CoOp which extensively uses the toolbox Dassl.pytorch so you need to install the dassl environment first. Simply follow the instructions described here to install dassl. After that, run pip install -r requirements.txt to install a few more packages (this should be done when dassl is activated). Then, you are ready to go.

Data

Follow DATASETS.md to install the datasets. After successfully set up the datasets, the data directory variables, DATA, in each script under /apt/scripts MUST be updated with the root path of those datasets.

Pre-trained Robust CLIP Backbone

We adopt as backbone the pre-trained adversarially-robust CLIP models from TeCoA. The used pre-trained weights are provided here. To run the code, the pre-trained backbone models should be placed under the directory /backbone. The code currently supports two architectures: ViT-B/32 (named vitb32) and ResNet50 (named rn50). Taking an example of tuning ViT-B/32 at epsilon=4/255, the path to the checkpoint is /backbone/vitb32_eps4.pth.tar. Note that our code can be easily adapted to load other pre-trained models as backbone.

Adversarial Prompt Tuning

The following command runs APT to tune text prompt using an experiment setting specified by APT/configs/trainers/APT/vit_b32_ep50.yaml:

bash scripts/APT.sh imagenet vit_b32_ep50 end 16 16 False 4 2.67 3 0 onfly 0

The above arguments correspond to in order:

1. dataset ID. The list of supported dataset ID is given by the name of dataset source code files under the directory APT/datasets.
2. training configuration identifier. For full specification, please refer to the corresponding file. There are other predefined configurations under APT/configs/trainers/APT.
3. the position of class token
4. the number of context vectors, M
5. the number of shots, N, -1 for tuning with entire training set.
6. the variant of APT: True for Class Specific Context (CSC); False for Unified Context (UC)
7. the training perturbation budget, \epsilon
8. the step size of training adversary, \alpha
9. the number of steps for training adversary
10. seed of run.
11. the prompting strategy: "perturbed", "constant" and "onfly"
12. the step size, \alpha, for perturbing text prompt if prompting strategy "perturbed" used.

Pre-trained Prompt Weights:

To facilitate reproducibility, the pre-trained text prompt weights are provided here.

Adversarial Evaluation

In-Distribution

The following command evaluates the tuned text prompt against PGD attack on the test set of the same dataset as training:

python evaluate.py path_to_checkpoint --cls-prompt prompter --attack pgd

• Replace the path_to_checkpoint with the real path ending by the seed-level directory, e.g., output/imagenet/CoOpAT/vit_b32_ep50_st_16shots/nctx16_cscFalse_ctpend/eps4_alpha2.67_step3/seed0.
• By default, the same perturbation budget is used for evaluation as for training which is read from the saved configuration file.
• --cls-prompt specifies the text prompt for classification (inference). By specifying prompter, the prompt is loaded from the saved, APT-tuned, weights. Otherwise, a string template is expected, e.g., "a photo of a {}" where {} is necessary and will be automatically replaced by the real class label.
• --atk-prompt specifies the text prompt for attack, i.e., generating adversarial examples. By default, it uses the same prompt as the --cls-prompt unless specified otherwise.
• The current code implements 4 adversarial attacks: PGD, TPGD, CW and AutoAttack. They are identified in --attack by pgd, tpgd, cw and aa, respectively.

The evaluation result will be saved under the provided path_to_checkpoint directory in a name of evaluation.yaml.

Zero-shot / Out-Of-Distribution

The following command evaluates the tuned text prompt against PGD attack on the other dataset, OxfordFlowers in this case:

python evaluate.py path_to_checkpoint --dataset --cls-prompt prompter --attack pgd

• --dataset specifies the target dataset to be evaluated on. Note that the naming of datasets is different from the ones defined here.

The evaluation result will be saved under the provided path_to_checkpoint directory in a name of dist_shift.yaml.

Dependency

The code is heavily built on top of the following projects:

• CoOp
• OODRobustBench

We sincerely appreciate their help!

Citation

@inproceedings{li2024apt,
    title={One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models},
    author={Lin Li, Haoyan Guan, Jianing Qiu, Michael Spratling},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2024}
}