Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?

INTERPRETABILITY" AmI (Attacks meet Interpretability) is an "attribute-steered" defense [3] to detect [1] adversarial examples [2] on facerecognition models.By applying interpretability techniques to a pre-trained neural network, AmI identifies "important" neurons. It then creates a second augmented neural network with the same parameters but increases the weight activations of important neurons. AmI rejects inputs where the original and augmented neural network disagree.