Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Understand

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification.

• However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images.
• We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks.
• Defense-GAN is trained to model the distribution of unperturbed images.