No description, website, or topics provided.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
dataset
ensemble_defense
iter_target
pgd
LICENSE
README.md
commands.txt
download_data.sh
extract_targets.py
run_attacks_and_defenses.py
run_attacks_and_defenses.sh

README.md

NIPS 2017 Competition on Adversarial Attacks and Defenses

Submissions to the non-targeted attack, targeted attack, and defense tracks for the NIPS Competition

Non-Targeted Attack

Directory: pgd/

Implemented a weighted ensembled version of the PGD attack introduced by Madry et al.

Weighted Ensemble: (0.35,Inception v3), (0.25,Ensemble Adversarially trained Inception ResNet v2), (0.2,Adversarially trained Inception v3), (0.1,Inception ResNet v2), (0.1,ResNet V2-152)

Final Placement: 14/91

Targeted Attack

Directory: iter_target/

Implemented a ensembled version of the iterative target class attack introduced by Kurakin et al.

Ensemble: Inception v3, Ensemble Adversarially trained Inception ResNet v2, Adversarially trained Inception v3

Instead of ensembling by each iteration averaging the losses (like what was done for non-targeted), generated adversarial examples individually on each model and averaged the resulting solutions.

Final Placement: 6/65

Defense

Directory: ensemble_defense/

Performed JPEG-Compression with quality=25 on the adversarial images, fed images to the following weighted ensemble. The mode of the predictions was taken.

Weighted Ensemble: (0.3,Inception ResNet v2), (0.3,Ensemble Adversarially trained Inception ResNet v2), (0.25,ResNet V2-152), (0.15,Adversarially trained Inception v3)

JPEG-Compression quality chosen in order to trade-off reducing the power of strong adversarial examples, while not harming the predictions on clean/weak images.

Final Placement: 11/107

Experiment

Download DEV dataset

python dataset/download_images.py --input_file=dataset/dev_dataset.csv --output_dir=dataset/images/

Download model_checkpoints needed for the non-targeted attack, targeted attack, and defense by navigating to the model_ckpts folder in each directory, and executing 'download_all.sh'

Generate adversarial images, and evaluate results on a set of undefended models with simple image processing defenses available by executing the commands provided in 'commands.txt'. Docker required, Nvidia-docker if GPUs would like to be used.