Intriguing properties of neural networks

Discovered adversarial examples: imperceptible input perturbations that cause state-of-the-art deep networks to misclassify with high confidence.

Directly guides deployment choices and architecture selection for task-adversarial-robustness.

Assumptions

• Standard empirical regularity and statistical stability hold across evaluation domains

Limitations

• Performance characteristics depend on domain distribution and compute allocation parameters