Defense-Resistant Backdoor Attacks Against Deep Neural Networks in Outsourced Cloud Environment
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2021.3087237
Abstract: The time and monetary costs of training sophisticated deep neural networks are exorbitant, which motivates resource-limited users to outsource the training process to the cloud. Concerning that an untrustworthy cloud service provider may inject backdoors… read more here.
Keywords: defense strategies; deep neural; neural networks; defense ... See more keywords
Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Network"
DOI: 10.1109/mnet.011.2000783
Abstract: Federated learning enables distributed training of deep learning models among user equipment (UE) to obtain a high-quality global model. A centralized server aggregates the updates submitted by UEs without knowledge of the local training data… read more here.
Keywords: federated learning; model dependent; backdoor attacks; coordinated backdoor ... See more keywords
Interpretability-Guided Defense Against Backdoor Attacks to Deep Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3111123
Abstract: As an emerging threat to deep neural networks (DNNs), backdoor attacks have received increasing attentions due to the challenges posed by the lack of transparency inherent in DNNs. In this article, we develop an efficient… read more here.
Keywords: deep neural; neural networks; backdoor attacks; interpretability guided ... See more keywords
Invisible Backdoor Attacks on Deep Neural Networks Via Steganography and Regularization
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2020.3021407
Abstract: Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific input (called triggers), trick the model into producing… read more here.
Keywords: deep neural; neural networks; backdoor attacks; invisible backdoor ... See more keywords
Stealthy and Flexible Trojan in Deep Learning Framework
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2022.3164073
Abstract: Deep neural networks (DNNs) are increasingly used as the critical component of applications, bringing high computational costs. Many practitioners host their models on third-party platforms. This practice exposes DNNs to risks: A third party hosting… read more here.
Keywords: backdoor attacks; framework; deep learning; model ... See more keywords
Automated Segmentation to Make Hidden Trigger Backdoor Attacks Robust against Deep Neural Networks
Sign Up to like & getrecommendations! Published in 2023 at "Applied Sciences"
DOI: 10.3390/app13074599
Abstract: The successful outcomes of deep learning (DL) algorithms in diverse fields have prompted researchers to consider backdoor attacks on DL models to defend them in practical applications. Adversarial examples could deceive a safety-critical system, which… read more here.
Keywords: trigger backdoor; backdoor attacks; segmentation; backdoor ... See more keywords