Revisiting the Auxiliary Data in Backdoor Purification

Abstract

This study examines how the choice of auxiliary data affects backdoor purification in realistic settings. It proposes Guided Input Calibration (GIC), a learnable transformation that aligns auxiliary data with the victim model’s original data distribution and improves purification across varied data sources.

Citation