Can Attention Masks Improve Adversarial Robustness?

Understand

Deep Neural Networks (DNNs) are known to be susceptible to adversarial examples.

• Adversarial examples are maliciously crafted inputs that are designed to fool a model, but appear normal to human beings.
• Recent work has shown that pixel discretization can be used to make classifiers for MNIST highly robust to adversarial examples.
• However, pixel discretization fails to provide significant protection on more complex datasets.