adversarial-attacks-white-black-box

Adversarial_Attacks description

Create imperceptible pertubations on an input image to fool the model (here mobilenetv2 pretrained on imagenet) to misclassify it as another class (for example an image of a "pug" which is a dog breed at index 254).

repo: https://github.com/sifubro/Adversarial_Attacks

Usage

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conda create --name virtenvname python=3.9

conda activate virtenvname

pip install adversarial-attacks-white-black-box==0.1.7

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Experimentation

For all attacks and experimentation done please see the notebook adversarial_attack_experimentation.ipynb.

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Main script

For a command line script do the following:

a) Targeted FGSM attack

This will run the simplest form of attack: Iterative FGSM targeted attack to fool the model into classifying an input image (here of a cat.jpg) to that of a pug (dog breed). The index 254 corresponds to the index of a "pug" in the imagenet dataset (see imagenet_class_list.md).

adversarial-attacks --input_img_path ./cat.jpg --output_path ./results_fgsm --target_class 254 --learning_rate 0.01 --sign_grad True --adv_iterations 30

Results will be saved in ./results_fgsm

Remark If you are having trouble just go in the subdirectory adversarial_attacks_white_black_box and run:

python main.py --input_img_path ./cat.jpg --target_class 254 --learning_rate 0.01 --sign_grad True --adv_iterations 30

(remember first to pip install the requirements.txt)

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b) FGSM attack masking background

This will run FGSM only on the foregound object (main one) while masking the background during optimization

adversarial-attacks --input_img_path ./cat.jpg --output_path ./results_mask_background --attack_method FGSMMaskBackground --target_class 254 --mask_background True --learning_rate 0.05 --sign_grad True --adv_iterations 10

Results will be saved in ./results_mask_background

TODO: Do the reverse

c) Zeroth Order Optimization Strategy

This will run a Black Box attack without assuming we have access to the gradients of the model. We estimate the zeroth-order gradient by using 2 perturbed samples.

adversarial-attacks --input_img_path ./cat.jpg --output_path ./results_zoo --attack_method ZerothOrderOptimization --target_class 254 --epsilon 0.05 --learning_rate 0.1 --add_noise True --noise_max_val 0.01 --sign_grad True --adv_iterations 30

Results will be saved in ./results_zoo

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d) Natural Evolution Strategies

TODO

This will run a Black Box attack without assuming we have access to the gradients of the model. We estimate the gradient by using a family of perturbations (e.g. Gaussian)

Results will be saved in ./results_nes

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e) FGSM on superpixels

TODO

Results will be saved in ./results_fgsm_superpixel

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For experimentation with more attacks see adversarial_attack_experimentation.ipynb

Module explanation

• adversarial_attack_experimentation.ipynb: This is a un-cleaned development notebook containing basic ideas and code. Needs to be refactored. For clean scripts and command line interface see below!
• adversarial_attack_base.py: Defines Abstract class (TODO: to convert to ABC) so that each attack can inherit from
• helper_functions.py: Basic helper functions for preprocessing input, decoding predictions, postprocessing and visualization.
• imagenet_class_list.md: ImageNet classification indicies for each class (254=pug for example)
• main.py: Main script for command line usage

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TODO list:

• Add additional abstract methods (convert AdversarialAttack class to ABC) so that each attack can inherit from
• experiment more with attacks (e.g. NES, modifying foreground/background and superpixels)
• Introduce logging module
• Add typing for each function
• Dockerize implementation
  • create Dockerfile and run inside a container to ensure same packages
  • serve the methods

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Instructions on how to create the package

python setup.py sdist bdist_wheel

pip install twine

twine upload dist/*

if failed login to PyPI account https://pypi.org/account/login/

and the setting "Add API token" -> copy and configue twine as follows

[pypi]

username = your_username

password = API-token

(for windows under C:\Users\Username)