ESMA

Pytorch implementation of paper "Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability", Junqi Gao, Biqing Qi, Yao Li, Zhichang Guo, Dong Li, Yuming Xing, Dazhi Zhang. The following prerequisites we used:

• Python (version 3.11.4)
• Pytorch (version 1.11.0+cu113)
• torchvision (version 0.12.0+cu113)
• timm (version 0.9.2)

Step 1: Pretrain Embedding

The classification model we used was trained by fine-tuning a pre-trained model using the SGD optimizer with a learning rate of 1e-3 on the ImageNet dataset. We employed early stopping with a patience of 5 during training.

To pretrain the embeddings using a specific source model (e.g., ResNet50), run the following command:

python ESMA.py --batch_size 25 --Source_Model ResNet50 --state pretrain_embedding

Step 2: Train ESMA

To train the ESMA model using the pre-trained embedding, run the following command:

python ESMA.py --batch_size 25 --Source_Model ResNet50 --epoch 300 --training_load_weight ckpt_pretrained_ResNet50_.pt --q 2 --state train_model

Step 3: Adversarial Testing

To perform adversarial testing using the trained ESMA model, run the following command:

python ESMA.py --batch_size 25 --Source_Model ResNet50 --test_load_weight ckpt_299_ResNet50_.pt --state advtest