Semantic Adversarial Examples

Understand

Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model.

• Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error.
• Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images.
• This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations.