Reverse Engineering of Imperceptible Adversarial Image Perturbations

Understand

It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary.

• There has been a vast volume of research to generate and defend such adversarial attacks.
• However, the following problem is left unexplored: How to reverse-engineer adversarial perturbations from an adversarial image? This leads to a new adversarial learning paradigm--Reverse Engineering of Deceptions (RED).
• If successful, RED allows us to estimate adversarial perturbations and recover the original images.