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Poisoning Web-Scale Training Datasets is Practical
May 7, 2024, 4:45 a.m. | Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian T
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep learning models are often trained on distributed, web-scale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionally introduce malicious examples to a model's performance. Our attacks are immediately practical and could, today, poison 10 popular datasets. Our first attack, split-view poisoning, exploits the mutable nature of internet content to ensure a dataset annotator's initial view of the dataset differs from the view downloaded by subsequent clients. …
abstract arxiv attacks cs.cr cs.lg dataset datasets deep learning distributed examples internet paper performance poisoning attacks popular practical scale s performance split training training datasets type view web
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