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
Speakers: Katharine Jarmul