Due to the model aging problem, Deep Neural Networks (DNNs) need updates...
The backdoor attack, where the adversary uses inputs stamped with trigge...
Self-supervised learning in computer vision trains on unlabeled data, su...
Most existing methods to detect backdoored machine learning (ML) models ...
Deep Learning backdoor attacks have a threat model similar to traditiona...
We conduct a systematic study of backdoor vulnerabilities in normally tr...
Deep Neural Networks are vulnerable to Trojan (or backdoor) attacks.
Rev...
Federated Learning (FL) is a distributed learning paradigm that enables
...
This paper finds that contrastive learning can produce superior sentence...
Deep neural networks are vulnerable to Trojan attacks. Existing attacks ...
With Deep Neural Network (DNN) being integrated into a growing number of...
Deep Neural Networks (DNNs) can learn Trojans (or backdoors) from benign...
We develop a novel optimization method for NLPbackdoor inversion. We lev...
Model compression can significantly reduce sizes of deep neural network ...
Backdoor attack injects malicious behavior to models such that inputs
em...
Back-door attack poses a severe threat to deep learning systems. It inje...
Trojan (backdoor) attack is a form of adversarial attack on deep neural
...
Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to...
Machine learning (ML) has progressed rapidly during the past decade and ...
Machine learning (ML) has made tremendous progress during the past decad...
Deep learning models are widely used for image analysis. While they offe...
Adversarial sample attacks perturb benign inputs to induce DNN misbehavi...