TARE: Weigh a Never-Poisoned Twin Before Reading Backdoor-Defense Costs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
The paper investigates how the choice of feature space affects the effectiveness of label‑only backdoor detection. By keeping the test, labels, and metric constant while swapping feature spaces, the authors show that defenses can perform very differently—e.g., a nearest‑neighbor check yields AUROC 0.546 on poisoned features but 0.995 on frozen DINOv2 features. They also demonstrate that higher detection scores do not necessarily translate to safer models and that static prototype‑based audits can be bypassed by adaptive attackers.
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