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Found 3 papers, 1 papers with code
Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning
In this paper, we explore the possibility of a less-cost defense from the perspective of model unlearning, that is, whether the model can be made to quickly \textbf{u}nlearn \textbf{b}ackdoor \textbf{t}hreats (UBT) by constructing a small set of poisoned samples.
Does Few-shot Learning Suffer from Backdoor Attacks?
However, in this paper, we propose the Few-shot Learning Backdoor Attack (FLBA) to show that FSL can still be vulnerable to backdoor attacks.
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
This survey explores the landscape of the adversarial transferability of adversarial examples.
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