Improving Network Robustness against Adversarial Attacks with Compact Convolution
Castillo, Carlos D.
arXiv e-Print archive - 2017 via Local Bibsonomy
Ranjan et al. propose to constrain deep features to lie on hyperspheres in order to improve robustness against adversarial examples. For the last fully-connected layer, this is achieved by the L2-softmax, which forces the features to lie on the hypersphere. For intermediate convolutional or fully-connected layer, the same effect is achieved analogously, i.e., by normalizing inputs, scaling them and applying the convolution/weight multiplication. In experiments, the authors argue that this improves robustness against simple attacks such as FGSM and DeepFool.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).