MetaSSA

Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability

Requirements

• python 3.9
• torch 1.8
• pretrainedmodels 0.7
• numpy 1.19
• pandas 1.2

Implementation

• Prepare models

  Download pre-trained PyTorch models here, which are converted from widely used Tensorflow models. Then put these models into ./models/

• Generate adversarial examples under inception-v3 -

  CUDA_VISIBLE_DEVICES=gpuid  python main.py --model_type inceptionv3

• Evaluations on normally trained and AT models

  python verify.py

• Evaluations on other defenses

  To evaluate the attack success rates on six more advanced models (HGD, R&P, NIPS-r3, RS, JPEG, NRP).

  • Inc-v3_ens3,Inc-v3_ens4,IncRes-v2_ens: You can directly run verify.py to test these models.

  • HGD, R&P, NIPS-r3: We directly run the code from the corresponding official repo.

  • RS: noise=0.25, N=100, skip=100. Download it from the corresponding official repo.

  • JPEG: No extra parameters.

  • NRP: purifier=NRP, dynamic=True, base_model=Inc-v3_ens3. Download it from the corresponding official repo.

    More details about evaluations on six more advanced in third_party