Poisoning Web-Scale Training Datasets is Practical

Thu 23Mar2023

Florian Tramèr, ETH Zürich

From 11:30 until 13:00

At CAB H 52 (Seminar) + CNB/F/110 (Lunch) , ETH Zurich

CAB H 52 (Seminar) + CNB/F/110 (Lunch), ETH Zurich

Abstract:

Deep learning models are often trained on distributed, webscale datasets crawled from the internet. We introduce two new dataset poisoning attacks that intentionally introduce malicious examples to degrade a model's performance. Our attacks are immediately practical and could, today, poison 10 popular datasets. We will discuss how the attacks work; why (we think) these haven't been exploited yet; and why defending against them comes with non-negligible costs.

Join us in CAB H 52 (Seminar) + CNB/F/110 (Lunch).

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