"On Detecting Adversarial Perturbations" train a second DNN, which takes the weights of the classifier to detect whether the internal representation of the image can be classified as an adversarial sample
There is actually an interesting approach to reduce the dimensionality using 1x1 convolution: CNN Filter Encoding
@FlorianPfisterer : Attacks might be get better if we just add random noise to them.
@doktorgibson
ML Engine is bad documented, is not newbie-friendly and requires preprocessing of data. Therefore we will stick to our former approach with VM instances.
4. Working Group Meeting (9. July 2018)
(aka. "Distributed development: Berlin, Stuttgart, Karlsruhe")
Work Items
@doktorgibson in #11 and #9
@simsso
@FlorianPfisterer