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An introduction to adversarial attacks and defences

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If you have a question about this talk, please contact Adrià Garriga Alonso.

AI safety is not limited to RL settings. For example, we can use machine learning algorithms to design spam filters, yet attackers can still “reverse-engineer” our defence to send us junk emails. Autonomous driving systems based on computer vision techniques are also vulnerable to attacks, for instance, attackers can carefully apply a sticker to a stop sign in order to fool the vision system of the car. In this talk I will briefly discuss the mathematical framework of these attack techniques (specifically on image classifiers) and defence techniques against them.

Slides available here: http://yingzhenli.net/home/pdf/attack_defence.pdf

This talk is part of the Engineering Safe AI series.

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