IAM

21thSEPTEMBER2019

READING

Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo, Rama Chellappa. Improving Network Robustness against Adversarial Attacks with Compact Convolution. CoRR abs/1712.00699 (2017).

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 on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: