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Found 4 papers, 2 papers with code
Backdoor Defense via Test-Time Detecting and Repairing
In this paper we investigate the possibility of defending against backdoor attacks by utilizing test-time partially poisoned data to remove the backdoor from the model.
Rethinking Local Perception in Lightweight Vision Transformer
The combination of the AttnConv and vanilla attention which uses pooling to reduce FLOPs in CloFormer enables the model to perceive high-frequency and low-frequency information.
Ranked #579 on
Image Classification
on ImageNet
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
To reduce the training time, we further develop SAC-m that selects CutMix Augmented samples as model inputs, without the need for training the surrogate models or generating adversarial examples.
Few-shot Backdoor Defense Using Shapley Estimation
Deep neural networks have achieved impressive performance in a variety of tasks over the last decade, such as autonomous driving, face recognition, and medical diagnosis.
