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Found 4 papers, 1 papers with code
On Feasibility of Server-side Backdoor Attacks on Split Learning
Recent studies demonstrate that collaborative learning models, specifically federated learning, are vulnerable to security and privacy attacks such as model inference and backdoor attacks.
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data
Deep neural networks (DNNs) have demonstrated remarkable performance across various tasks, including image and speech recognition.
Ranked #1 on
Image Classification
on DVS128 Gesture
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks
This paper proposes a backdoor detection method by utilizing a special type of adversarial attack, universal adversarial perturbation (UAP), and its similarities with a backdoor trigger.
Watermarking Graph Neural Networks based on Backdoor Attacks
The experiments show that our framework can verify the ownership of GNN models with a very high probability (up to $99\%$) for both tasks.
