Skip to content

Maximally separating features in intermediate feature layers using PCL loss + image transformations with adversarial example transferability.

Notifications You must be signed in to change notification settings

shrebox/Proactive-and-Reactive-Measures-for-Adversarial-Defense

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Proactive-and-Reactive-Measures-for-Adversarial-Defense

In this project we aim to study the effects of reactive and proactive attacks as an adversarial defense particularly ona base reactive defense (PCL as termed in paper refer) that maximally separates features in intermediate layers in a deep learning model. Also, we study the effects of image transformations on feature space and adversarial example transferability.

About

Maximally separating features in intermediate feature layers using PCL loss + image transformations with adversarial example transferability.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 77.0%
  • Python 23.0%