BIOLOGICALLY INSPIRED MECHANISMS FOR ADVERSARIAL ROBUSTNESS

Required Libraries

Python 3.6.8

tensorflow-gpu 2.0.0, tensorflow-determinism 0.3.0, numpy 1.17.2, scipy 1.3.3, jupyter 1.0.0, notebook 6.0.2, ipython 7.11.1, tqdm 4.40.2, Pillow 6.2.1, matplotlib 3.1.2, seaborn 0.9.0, randomgen 1.14.4, foolbox 2.3.0 (see note below)

notes:

do not install the foolbox package, instead move the foolbox source code to the parent directory containing this project folder. such that:

• parent folder
  • foolbox
    • ...
  • adversary
    • ...

Preparing Datasets

• CIFAR10: Nothing needs to be done.
• IMAGENET10: Move 10 classes of the ImageNet training split to folder adversary/imagenet10/train, validation split to folder adversary/imagenet10/val, bounding boxes to folder adversary/imagenet10/bbox
• IMAGENET100: Move shortlist.pickle (list of images to include in the dataset), move all classes of ImageNet training split to ../data/ImageNet/raw-data/train and validation split to ../data/ImageNet/raw-data/validation.

Training a Model

• training a standard resnet model on cifar10:

python trainer.py --name=CIFAR_RESNET --model=resnet_cifar --dataset=cifar10 --augment=1 --sampling=0 --coarse_fixations=0 --auxiliary=0 --only_evaluate=0

• training a coarse fixations model on cifar10:

python trainer.py --name=CIFAR_FIXATIONS --model=resnet_cifar --dataset=cifar10 --augment=1 --sampling =0 --coarse_fixations=1 --auxiliary=0 --only_evaluate=0

• training a retinal fixations model on cifar10:

python trainer.py --name=CIFAR_SAMPLING --model=resnet_cifar --dataset=cifar10 --augment=1 --sampling= 1 --coarse_fixations=0 --auxiliary=0 --only_evaluate=0

• training a cortical fixations model on cifar10:

python trainer.py --name=CIFAR_ECNN --model=resnet_cifar --dataset=cifar10 --augment=1 --sampling=0 --coarse_fixations=0 --auxiliary=1 --cifar_ecnn=1 --only_evaluate=0

• training a standard resnet model on imagenet10:

python trainer.py --name=CNNwoSAMPLINGwoFIXATIONS --model=resnet --dataset=imagenet10 --augment=1 --sampling=0 --coarse_fixations=0 --auxiliary=0 --only_evaluate=0

• training a coarse fixations model on imagenet10:

python trainer.py --name=CNNwoSAMPLINGwFIXATIONS --model=resnet --dataset=imagenet10 --augment=1 --sampling=0 --coarse_fixations=1 --auxiliary=0 --only_evaluate=0

• training a retinal fixations model on imagenet10:

python trainer.py --name=CNNwSAMPLINGwoFIXATIONS --model=resnet --dataset=imagenet10 --augment=1 --sampling=1 --coarse_fixations=0 --auxiliary=0 --only_evaluate=0

• training a cortical fixations model on imagenet10:

python trainer.py --name=ECNNwoSAMPLINGwAUXILIARY --model=ecnn --dataset=imagenet10 --augment=1 --sampling=0 --coarse_fixations=0 --auxiliary=1 --only_evaluate=0

For training models on ImageNet100, use the commands for ImageNet10 but replace the argument for dataset with imagenet100 instead of imagenet10.

Evaluating a Model (Standard Performance)

• evaluating the standard performance of a standard resnet model on cifar10:

python trainer.py --name=CIFAR_RESNET --model=resnet_cifar --dataset=cifar10 --augment=0 --sampling=0 --coarse_fixations=0 --auxiliary=0 --only_evaluate=1

• evaluating the standard performance of a coarse fixations model on cifar10:

python trainer.py --name=CIFAR_FIXATIONS --model=resnet_cifar --dataset=cifar10 --augment=0 --sampling =0 --coarse_fixations=1 --auxiliary=0 --only_evaluate=1

• evaluating the standard performance of a retinal fixations model on cifar10:

python trainer.py --name=CIFAR_SAMPLING --model=resnet_cifar --dataset=cifar10 --augment=0 --sampling=1 --coarse_fixations=0 --auxiliary=0 --only_evaluate=1

• evaluating the standard performance of a cortical fixations model on cifar10:

python trainer.py --name=CIFAR_ECNN --model=resnet_cifar --dataset=cifar10 --augment=0 --sampling=0 --coarse_fixations=0 --auxiliary=0 --cifar_ecnn=1 --only_evaluate=1

• evaluating the standard performance of a standard resnet model on imagenet10:

python trainer.py --name=CNNwoSAMPLINGwoFIXATIONS --model=resnet --dataset=imagenet10 --augment=0 --sampling=0 --coarse_fixations=0 --auxiliary=0 --only_evaluate=1

• evaluating the standard performance of a coarse fixations model on imagenet10:

python trainer.py --name=CNNwoSAMPLINGwFIXATIONS --model=resnet --dataset=imagenet10 --augment=0 --sampling=0 --coarse_fixations=1 --auxiliary=0 --only_evaluate=1

• evaluating the standard performance of a retinal fixations model on imagenet10:

python trainer.py --name=CNNwSAMPLINGwoFIXATIONS --model=resnet --dataset=imagenet10 --augment=0 --sampling=1 --coarse_fixations=0 --auxiliary=0 --only_evaluate=1

• evaluating the standard performance of a cortical fixations model on imagenet10:

python trainer.py --name=ECNNwoSAMPLINGwAUXILIARY --model=ecnn --dataset=imagenet10 --augment=0 --sampling=0 --coarse_fixations=0 --auxiliary=1 --only_evaluate=1

For evaluating the standard performance of models on ImageNet100, use the commands for ImageNet10 but replace the argument for dataset with imagenet100 instead of imagenet10.

Evaluating a Model (Adversarial Robustness)

see below on notes for customizing adversarial robustness evaluation

• evaluating the adversarial robustness of standard resnet model with default parameters on cifar10

python adversary.py --name=CIFAR_RESNET --model=resnet_cifar --dataset=cifar10 --sampling=0 --coarse_fixations=0 --evaluate_mode=robustness

• evaluating the adversarial robustness of coarse fixations model on cifar10

python adversary.py --name=CIFAR_FIXATIONS --model=resnet_cifar --dataset=cifar10 --sampling=0 --coarse_fixations=1 --evaluate_mode=robustness

• evaluating the adversarial robustness of retinal fixations model on cifar10

python adversary.py --name=CIFAR_SAMPLING --model=resnet_cifar --dataset=cifar10 --sampling=1 --coarse_fixations=0 --evaluate_mode=robustness

• evaluating the adversarial robustness of cortical fixations model on cifar10

python adversary.py --name=CIFAR_ECNN --model=resnet_cifar --dataset=cifar10 --sampling=0 --coarse_fixations=0 --cifar_ecnn=1 --evaluate_mode=robustness

• evaluating the adversarial robustness of standard resnet model on imagenet10

python adversary.py --name=CNNwoSAMPLINGwoFIXATIONS --model=resnet --dataset=imagenet10 --sampling=0 --coarse_fixations=0 --evaluate_mode=robustness

• evaluating the adversarial robustness of coarse fixations model on imagenet10

python adversary.py --name=CNNwoSAMPLINGwFIXATIONS --model=resnet --dataset=imagenet10 --sampling=0 --coarse_fixations=1 --evaluate_mode=robustness

• evaluating the adversarial robustness of retinal fixations model on imagenet10

python adversary.py --name=CNNwSAMPLINGwoFIXATIONS --model=resnet --dataset=imagenet10 --sampling=1 --coarse_fixations=0 --evaluate_mode=robustness

• evaluating the adversarial robustness of cortical fixations model on imagenet10

python adversary.py --name=ECNNwoSAMPLINGwAUXILIARY --model=ecnn --dataset=imagenet10 --sampling=0 --coarse_fixations=0 --evaluate_mode=robustness

Optional Adversarial Robustness Arguments:

• attack_algo: attack algorithm to use ('PGD' or 'FGSM' or 'PGD_ADAM')
• attack_iterations: number of iterations for iterative algorithm (set to 1 for FGSM)
• attack_step_size: step size for algorithm
• attack_distance_metric: Lp norm for distance metrics and algorithm variants ('LINF' or 'L2' or 'L1')
• attack_criteria_targeted: use a targeted or an untargeted loss for the algorithm
• attack_criteria_det: determine adversarial class for a targeted loss in a deterministic fashion or randomly (randomly is not implemented yet)
• attack_random_init: add a random noise to the image before running algorithm

For evaluating the adversarial robustness of models on ImageNet100, use the commands for ImageNet10 but replace the argument for dataset with imagenet100 instead of imagenet10.

Explore Pre-Calculated Adversarial Robustness

We also provide directly the results of training and evaluating the adversarial robustness of the models. See all files with the extension .packet in the folders at adversary/cluster_runs/adversary/final_store/.

The packets are dictionaries stores as pickles. See adversary.py for how these dictionaries were generated and playground/PAPER_PLOTS_V2.ipynb notebook for how these dictionaries were read and parsed to generate the plots and tables in the paper.

Evaluating a Model (Feature Robustness, Etc.)

work in progress. :)

Models Checkpoints

https://www.dropbox.com/s/fph11yxcgntq1rv/model_checkpoints.7z?dl=0

Paper

https://arxiv.org/abs/2006.16427