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Found 7 papers, 7 papers with code
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
Backdoor attack poses a significant security threat to Deep Learning applications.
Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation and Echopraxia
Large Language Models (LLMs) have become prevalent across diverse sectors, transforming human life with their extraordinary reasoning and comprehension abilities.
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training.
GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network
Recognizing this shortfall, there has been a burgeoning interest in recent years in harnessing the potential of Graph Neural Networks (GNN) to exploit the topological information by modeling features selected from each EEG channel in a graph structure.
Detecting Backdoors in Pre-trained Encoders
We show the effectiveness of our method on image encoders pre-trained on ImageNet and OpenAI's CLIP 400 million image-text pairs.
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense
Attack forensics, a critical counter-measure for traditional cyber attacks, is hence of importance for defending model backdoor attacks.
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting.
