Saliency based Adversarial Training (SAT)

Code for ECML-PKDD 2020 Paper: "On Saliency Maps and Adversarial Robustness"

Usage:

Train Teacher: python3 teacher.py --dataset [cifar10/cifar100] --model [ResNet34/ResNet10] --method [adv std] --bs [128] [--resume]

Train Student: python3 student.py --dataset [cifar10/cifar100] --model [ResNet34/ResNet10] --method [adv std] --bs [128] [--resume] --teacher [ResNet34_std/ResNet34_adv] --exp [gcam++/gbp,sgrad]

Note: There are some comments in file thats need to be uncommented for certain uses. Please go through them before running. More organised code to be followed soon.

Files:

• teacher.py: Training a teacher network in both non-robust or robust fashion.
• student.py: Training student network guided by teacher
• student_adv.py: Training student network adversarially guided by teacher
• student_ensemble.py: Training student network guided by saliency maps of two teacher
• teacher_[nadv/ntrades/trades/noise].py: Different training of teacher networks
• student_[bbox/bbox_adv/bbox_trades].py: specially for Tiny-imagenet and flower datasets where bounding boxes and segmentation masks are already available.