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Found 5 papers, 1 papers with code
Magnitude-based Neuron Pruning for Backdoor Defens
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment.
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment.
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
To improve the recall of the RAG for the target contexts, we introduce a knowledge graph to construct structured data to achieve hard matching at a fine-grained level.
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
We then pre-train a generative self-supervised graph autoencoder (GAE) to better learn the features of benign models in order to detect backdoor models without knowing the attack strategy.
DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstruction
We propose DAFAR, a feedback framework that allows deep learning models to detect/purify adversarial examples in high effectiveness and universality, with low area and time overhead.
