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FGSM(Fast Gradient Sign Method)


Overview

Simple pytorch implementation of FGSM and I-FGSM
(FGSM : explaining and harnessing adversarial examples, Goodfellow et al.)
(I-FGSM : adversarial examples in the physical world, Kurakin et al.)
overview

FGSM

FGSM

I-FGSM

IFGSM

Dependencies

python 3.6.4
pytorch 0.3.1.post2
visdom(optional)
tensorboardX(optional)
tensorflow(optional)

Usage

  1. train a simple MNIST classifier
python main.py --mode train --env_name [NAME]
  1. load trained classifier, generate adversarial examples, and then see outputs in the output directory
python main.py --mode generate --iteration 1 --epsilon 0.03 --env_name [NAME] --load_ckpt best_acc.tar
  1. for a targeted attack, indicate target class number using --target argument(default is -1 for a non-targeted attack)
python main.py --mode generate --iteration 1 --epsilon 0.03 --target 3 --env_name [NAME] --load_ckpt best_acc.tar

Results

Non-targeted attack

from the left, legitimate examples, perturbed examples, and indication of perturbed images that changed predictions of the classifier, respectively

  1. non-targeted attack, iteration : 1, epsilon : 0.03 non-targeted1
  2. non-targeted attack, iteration : 5, epsilon : 0.03 non-targeted2
  3. non-targeted attack, iteration : 1, epsilon : 0.5 non-targeted3

Targeted attack

from the left, legitimate examples, perturbed examples, and indication of perturbed images that led the classifier to predict an input as the target, respectively

  1. targeted attack(9), iteration : 1, epsilon : 0.03 targeted1
  2. targeted attack(9), iteration : 5, epsilon : 0.03 targeted2
  3. targeted attack(9), iteration : 1, epsilon : 0.5 targeted3

References

  1. explaining and harnessing adversarial examples, Goodfellow et al.
  2. adversarial examples in the physical world, Kurakin et al.