Results 231 to 240 of about 29,955 (264)

Noise-Adversarial Denoising of Motor Acoustic Signals Across Noise Domains. [PDF]

Sensors (Basel)
Yang Y, Xu J, Jiang X, Xiao X, Dong H.
europepmc   +1 more source

Research on Medical Image Super-Resolution Reconstruction Algorithm Based on Dilated Convolution and Multi-Module Fusion. [PDF]

J Imaging
Xu Z, Guo Y.
europepmc   +1 more source

CASCADENCE: a layered cascade defense mechanism for federated learning. [PDF]

Sci Rep
Hashmi SW, Shukla RM, Bhunia S.
europepmc   +1 more source

AI-Enabled Hardware-in-the-Loop Validation for Automotive Cybersecurity: A Review of Cyber Threats, Testbeds, and Intelligent Detection. [PDF]

Sensors (Basel)
Kordi F, Fortier P, Miled A.
europepmc   +1 more source

Fault Diagnosis of Ship Chilled Water Units Based on a Hybrid Attention Domain-Adaptive Network. [PDF]

Entropy (Basel)
Feng Q, Li Y, Liu Y, Liang X, Liu M, Qu D, Cen Y.   +6 more
europepmc   +1 more source

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Learning Universal Adversarial Perturbation by Adversarial Example

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li, Yanhua Yang, Kun Wei, Xu Yang 0019, Heng Huang 0001   +4 more
openaire   +2 more sources

Understanding adversarial robustness against on-manifold adversarial examples

Pattern Recognition
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One of the hypotheses of the existence of the adversarial examples is the off-manifold assumption: adversarial examples lie off the data manifold. However, recent research showed
Yanbo Fan
exaly   +3 more sources

On The Generation of Unrestricted Adversarial Examples

2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2020
Adversarial examples are inputs designed by an adversary with the goal of fooling the machine learning models. Most of the research about adversarial examples have focused on perturbing the natural inputs with the assumption that the true label remains unchanged.
Mehrgan Khoshpasand, Ali A. Ghorbani 0001   +1 more
openaire   +1 more source