SAGE: Similarity-Based Cleaning of Poisoned Training Data from Verified Examples
SAGE defends against data poisoning by using a small verified set to detect malicious examples in larger training data. It trains a feature extractor to group similar images and then uses similarity-based predictions against the verified set to flag suspicious samples, effectively cleaning the dataset while minimizing discarded examples.