Deep Learning in Adversarial Context
This thesis is about the adversarial attacks and defenses in deep learning. We propose to improve the performance of adversarial attacks in the aspect of speed, magnitude of distortion, and invisibility. We contribute by defining invisibility with smoothness and integrating it into the optimization of producing adversarial examples. We succeed in c...
