4. Working Group Meeting

[Simsso]

4. Working Group Meeting (9. July 2018)

(aka. "Distributed development: Berlin, Stuttgart, Karlsruhe")

Work Items

@doktorgibson in #11 and #9

• make cloud testing CNN ML-Engine compatible
• define interfaces for Google Cloud Storage (GCS)
• logging of images / text
• make TensorBoard work
• local execution with debugger

@simsso

• complete layerwise perturbation experiments #14
• implementation of attacks #17
• attack frameworks #20
• read "The Limitations of Deep Learning in Adversarial Settings", "On Detecting Adversarial Perturbations"

@FlorianPfisterer

• implementation of attacks #17
• analysis of last year's attacks (code) #19
• read "The Limitations of Deep Learning in Adversarial Settings", "On Detecting Adversarial Perturbations"

[ghost]

Minutes

@Simsso

• "The Limitations of Deep Learning in Adversarial Settings" can be disregarded nothing interesting
• "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.

@FlorianPfisterer

• "The Limitations of Deep Learning in Adversarial Settings" there are few measurements which could be considered during evaluation of defenses

Work Items

• Submit a test model to the CrowdAI page
• Upload and test a foolbox model
• Rank our classifier (https://tiny-imagenet.herokuapp.com/)
• Switch to VM instances and build the deployment pipeline using GCE