Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples

This repository is the official implementation of our paper Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples. In this work we propose CADE, a framework that can generate Counterfactual ADversarial Examples.

Example for latent

attacker = CADELatent(generative_model=generative_model, 
                      attacking_nodes=attacking_nodes, 
                      substitute=substitute, 
                      device=device)
                      
x_cade = attacker.attack_whitebox(x=x, 
                                  label=label, 
                                  lr=lr, 
                                  epochs=num_steps, 
                                  epsilon=epsilon,
                                  causal_layer=causal_layer)                  

Example for observable

attacker = CADEObservable(causal_dag=causal_dag, 
                          attacking_nodes=attacking_nodes, 
                          y_index=y_index, 
                          substitute=substitute,
			  l_dag=l_dag)
                          
x_cade = attacker.attack(endogenous=endogenous, 
                         epsilon=epsilon, 
                         causal_layer=causal_layer,
                         num_steps=num_steps,
                         lr=lr)                                                   

Experiment on Pendulum

To reproduce the result on Pendulum, run

python Experiment_CADE_Pendulum.py --substitute resnet50

python Experiment_CADE_Pendulum.py --substitute vgg16

Experiment on CelebA

To reproduce the result on CelebA, run

python Experiment_CADE_CelebA.py --substitute resnet50

python Experiment_CADE_CelebA.py --substitute vgg16

Experiment on SynMeasurement

To reproduce the result on SynMeasurement, run

python Experiment_CADE_SynMeasurement.py --substitute lin

python Experiment_CADE_SynMeasurement.py --substitute mlp

Demos on Pendulum and CelebA:

Demo_CADE_Pendulum.ipynb

Demo_CADE_CelebA.ipynb

Download the checkpoints for the experiment, please visit:

https://drive.google.com/drive/folders/1khGEtAqlpKtLFspSKdz5zyObwpDGoLzV?usp=share_link

Feel free to train the models from scratch:

The causal generative models for Pendulum and CelabA are trained based on this repository

Train the target models:

model_training/Pendulum/model_train_standard.py

model_training/Pendulum/model_train_adversarial.py

model_training/CelebA/model_train_standard.py

model_training/CelebA/model_train_adversarial.py

model_training/SynMeasurement/model_train_standard.py

model_training/SynMeasurement/model_train_adversarial.py