back

Deep Learning Blindspots

Tools for Fooling the "Black Box"

If you suspend your transcription on amara.org, please add a timestamp below to indicate how far you progressed! This will help others to resume your work!

Please do not press “publish” on amara.org to save your progress, use “save draft” instead. Only press “publish” when you're done with quality control.

Video duration: 00:53:48
Language: English
Abstract: In the past decade, machine learning researchers and theorists have created deep learning architectures which seem to learn complex topics with little intervention. Newer research in adversarial learning questions just how much “learning" these networks are doing. Several theories have arisen regarding neural network “blind spots” which can be exploited to fool the network. For example, by changing a series of pixels which are imperceptible to the human eye, you can render an image recognition model useless. This talk will review the current state of adversarial learning research and showcase some open-source tools to trick the "black box."

This talk aims to:

- present recent research on adversarial networks
- showcase open-source libraries for fooling a neural network with adversarial learning
- recommend possible applications of adversarial networks for social good

This talk will include several open-source libraries and research papers on adversarial learning including:

Intriguing Properties of neural networks (Szegedy et al., 2013): https://arxiv.org/abs/1312.6199
Explaining and Harnessing Adversarial Examples (Goodfellow et al., 2014) https://arxiv.org/abs/1412.6572
DeepFool: https://github.com/LTS4/DeepFool
Deeppwning: https://github.com/cchio/deep-pwning

Talk ID: 8860
Event:: 34c3
Day: 2
Room: Saal Adams
Start: 2 p.m.
Duration: 01:00:00
Track: Resilience
Type of: lecture
Speaker: Katharine Jarmul
Talk Slug & media link: 34c3-8860-deep_learning_blindspots