The code for the paper 'Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks' in ECCV 2022.

This project is tested under the following environment settings:

• OS: Ubuntu 18.04.4
• GPU: Geforce 2080 Ti or Tesla P100
• Python: 3.6
• PyTorch: >= 1.6.0
• Torchvision: >= 0.6.0

• Please prepare the ImageNet set, and we adopt the ImageNet training set as our training data.

• Below we provide running commands for training the conditional generator based on 8 different target classes from a previous same setting.

python train.py --train_dir $DATA_PATH/ImageNet/train --model_type incv3 --eps 16 --batch_size 64 --start-epoch 0 --nz 16 --epochs 10 --label_flag 'N8'

python train.py --train_dir $DATA_PATH/ImageNet/train --model_type res152 --eps 16 --batch_size 64 --start-epoch 0 --nz 16 --epochs 10 --label_flag 'N8'

Download pretrained adversarial generators Generator-Inv3 and Generator-Res152 based on the setting of 8 different classes.

Below we provide running commands for generating targeted adversarial examples on ImageNet NeurIPS validation set (1k images):

python eval_n8.py --data_dir data/ImageNet1k/ --model_type incv3 --eps 16 --load_path $SAVE_CHECKPOINT

The above crafted adversarial examples can be directly used for testing different models in torchvision. Besides, you can also adopt Inception ResNet-V2 , ResNet-V2-152 in Tensorflow Slim or Inc-v3_ens3, Inc-v3_ens4, IncRes-v2_ens trained by Ensemble Adversarial Training in Tensorflow.

Below we provide running commands for testing our method against different black-box models:

python inference_n8.py --test_dir $IMAGES_DIR --model_t vgg16

Besides, we also provide all 20 pretrained adversarial generators Generator-Inv3-1K.zip based on the basic setting of 1K classes.