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Experiments

The code is based on ZSKT.

Run Experiments

General

  • Single experiment. Distill from a badnet_grid-backoored teacher with arch WRN-16-2 to student with arch WRN-16-1. 
    export CUDA_VISIBLE_DEVICES=0  # specify GPU
python main.py --dataset=CIFAR10 --teacher_architecture=WRN-16-2 --student_architecture=WRN-16-1 --trigger_pattern=badnet_grid --seeds=3
  • Run sweeps. Choose a sweep command below where you can find all hparams. For example, 
    wandb sweep sweeps/cifar10_wrn_poi.yml
# get the `wandb agent <agent code>` from the CLI output.
wandb agent <agent code>  # this will run one pair of hyper-params from `cifar10_wrn_poi.yml`.
    wandb agent <> can be run in parallel in different processes, which will auto select different params in yml file.
Trigger Teacher Student Acc/ASR
Acc/ASR ZSKT ZSKT+ABD Clean KD
BadNets (grid) 92.1/99.9 71.9/96.9 68.3/0.7 74.6/4.3
Trojan WM 93.8/100 82.7/93.9 78.2/22.5 77.5/11.1
Trojan 3x3 93.4/98.7 80.9/96.8 71.7/33.3 72.9/1.7
Blend 93.9/99.7 77.0/74.4 71.5/23.1 78.0/4.3
Trojan 8x8 93.7/99.6 80.5/57.2 72.6/17.8 75.2/9.3
BadNets (sq) 93.4/97.8 80.8/37.8 77.9/1.9 76.2/9.1
CL 91.2/94.3 76.8/17.5 67.4/10.2 69.4/2.1
Sig 90.5/97.3 77.9/0.0 72.2/0. 77.4/0.
l2_inv 93.9/100 82.0/0.3 70.7/1.9 77.2/1.2
l0_inv 92.4/99.6 72.8/8.3 69.4/0. 79.2/3.7

Distill from poisoned teachers

Evaluate different backdoors with ZSKT.

wandb sweep sweeps/cifar10_wrn_poi.yml
wandb sweep sweeps/gtsrb_wrn_poi.yml

Distill using clean data

# single run
python kd_distill.py --trigger_pattern=badnet_grid --no_log
wandb sweep sweeps/cifar10_wrn_poi_distill.yml
wandb sweep sweeps/gtsrb_wrn_poi_distill.yml

Customization

Add dataset: Edit get_test_loader in zskt/datasets/datasets.py.

Add model: Edit zskt/models/selector.py to add new architecture and pre-trained model paths.

Defense

  • CIFAR10
wandb sweep sweeps/cifar10_wrn_poi_defense.yml
  • GTSRB
wandb sweep sweeps/gtsrb_wrn_poi_defense.yml