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Found 5 papers, 1 papers with code
Time-Distributed Backdoor Attacks on Federated Spiking Learning
Then, we evaluate the transferability of known FL attack methods to SNNs, finding that these lead to suboptimal attack performance.
Tabdoor: Backdoor Vulnerabilities in Transformer-based Neural Networks for Tabular Data
Our findings highlight the urgency of addressing such vulnerabilities and provide insights into potential countermeasures for securing DNN models against backdoors in tabular data.
Rethinking the Trigger-injecting Position in Graph Backdoor Attack
There is no work analyzing and explaining the backdoor attack performance when injecting triggers into the most important or least important area in the sample, which we refer to as trigger-injecting strategies MIAS and LIAS, respectively.
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
SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification
Nevertheless, it is vulnerable to backdoor attacks that modify the training set to embed a secret functionality in the trained model.
