README.md

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation (NeurIPS 2022)

PyTorch implementation for the state-of-art transfer attack: Reverse Adversarial Perturbation (RAP).

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

Zeyu Qin*, Yanbo Fan*, Yi Liu, Li Shen, Yong Zhang, Jue Wang, Baoyuan Wu

In NeurIPS 2022.

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Codes:

• rap_attack.py: full version

The examples:

• targeted attack with DI and logit loss from ResNet-50

  
  python /targeted_attack/rap_attack.py --num_data_augmentation 1  --targeted  --transpoint 400 --seed 9018 --source_model resnet_50 --loss_function MaxLogit --DI --max_iterations 300
  

• RAP targeted attack with DI and logit loss from ResNet-50

  python /targeted_attack/rap_attack.py --num_data_augmentation 1  --targeted  --transpoint 0 --seed 9018 --source_model resnet_50 --loss_function MaxLogit --DI --max_iterations 300
  

• RAP-LS targeted attack with DI and logit loss from ResNet-50

  python /targeted_attack/rap_attack.py --num_data_augmentation 1  --targeted  --transpoint 100 --seed 9018 --source_model resnet_50 --loss_function MaxLogit --DI --max_iterations 300
  

The parameters of config:

- targeted attack or not : --targeted or None
- source model: -- source_model (resnet_50, densenet, inception, vgg16)
- random seed: --seed 1234
- interation number of outer minimization: --max_iterations 
- MI or not: --MI or None
- DI or not: --DI or None
- TI or not: --TI or None
- SI or not: (--SI and --m2 5) or None 
- Admix or not: 
  (--m1 3 an --m2 5) or None
  --strength 0.2
- transpoint:
  --transpoint 400: baseline method
  --transpoint 0: baseline+RAP
  --transpoint 100: baseline+RAP-LS
- loss function: --loss_function: CE or MaxLogit for outer minimization
- epsilon of attacks: --adv_epsilon: 16/255, the perturbation budget for - inner maximization
  --adv_steps: 8, the step for inner maximization

This code is based on source code from NeurIPS 2021 paper , "On Success and Simplicity: A Second Look at Transferable Targeted Attacks". The used dataset is also contained in their repository. Please consider leaving a ⭐ on their repository.