EAN: 9783863598020

ROBUSTNESS ANALYSIS OF DEEP NEURAL NETWORKS IN ...

In this thesis  we study the robustness and generalization properties of Deep Neural Networks
(DNNs) under various noisy regimes  due to corrupted inputs or labels. Such corruptions can be
either random or intentionally crafted to disturb the target DNN. Inputs corrupted by
maliciously designed perturbations are known as adversarial examples and have been shown to
severely degrade the performance of DNNs. However  due to the non-linearity of DNNs  crafting
such perturbations is non-trivial. [...]

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