Search Results for author:
Found 8 papers, 3 papers with code
CredID: Credible Multi-Bit Watermark for Large Language Models Identification
This paper proposes a multi-party credible watermarking framework (CredID) involving a trusted third party (TTP) and multiple LLM vendors to address these issues.
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
Given the diversity of modalities, BackdoorMBTI facilitates systematic evaluation across different data types.
Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond
Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse.
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.
