SAGE: Similarity-Based Cleaning of Poisoned Training Data from Verified Examples
As machine learning increasingly relies on public, untrusted data sources, data poisoning attacks, which inject malicious examples into training data to induce misclassification of a chosen target, pose a growing threat. Existing defenses either assume zero ground-truth information about which examples are poisoned, or...
Chaeeun Han, Soodeh Atefi, Yevgeniy Vorobeychik et al.
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