This fork lets you use CLIP's vision transformer to generate FFT adversarial images ❓🤖‍💫

• python train.py --model clipvit

In utils/models.py (at the very end), you can select the CLIP model:

model, preprocess = clip.load("ViT-L/14")

Change that to e.g. "ViT-B/32" to use the smallest CLIP model if you want a quick(er) look.

• python mask_train.py --model clipvit --attack FMN --mask adversarial
• python mask_train.py --model clipvit --attack FMN --mask essential

--attack: Use FMN, PGD, DF

Fast Minimum Norm - Projected Gradient Descent - DeepFool

• python correlation.py --model clipvit --attack FMN

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👇 See the original readme.md for further details 👇

Relating Implicit Bias and Adversarial Attacks through Intrinsic Dimension

Code for the paper, tested with python>=3.8, <=3.10.

Required libraries are reported in requirements.txt and they can be installed via pip.

Data and models can be downloaded by running:

bash preparation.sh

Then, the first step is to obtain trained models, which can by done by typing:

python train.py --model

The model argument can be chosen between resnet20 (for CIFAR-10) and resnet18 and vit (for Imagenette). This script will create a trained_models folder containing the trained parameters for the chosen model, to be used for the following experiments.

Fourier masks (essential frequency masks and adversarial frequency masks) can be trained by running:

python mask_train.py --model --attack --mask

The first argument follows the same syntax as above, while attack determines the kind of adversarial attack to employ and it can be chosen among FMN, PGD and DF (for respectively Fast Minimum Norm , Projected Gradient Descent and DeepFool). mask defines the type of masks to be trained, either essential or adversarial.

Once both essential frequency masks and adversarial frequency masks have been computed for a given model-attack pair, correlations (based on cosine similarity and on the novel method based on Intrinsic Dimension) can be computed using:

python correlation.py --model --attack

The experiment on class-specificity of masks, reported in sections 4.7 and A.6, can be reproduced by running:

python class_specificity.py

And, finally, the class-level masks can be computed and tested running the notebook class_level_masks.ipynb.