Results 61 to 70 of about 25,829 (292)
Adversarial Detector with Robust Classifier
2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech), 2022 Deep neural network (DNN) models are wellknown to easily misclassify prediction results by using input images with small perturbations, called adversarial examples. In this paper, we propose a novel adversarial detector, which consists of a robust classifier and a plain one, to highly detect adversarial examples.
Takayuki Osakabe +3 more
openaire +2 more sources
Adversarial Self-Supervised Learning for Robust SAR Target Recognition
Remote Sensing, 2021 Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years.
Yanjie Xu +5 more
doaj +1 more source
Learning Discriminative Features for Adversarial Robustness
, 2022 Deep Learning models have shown incredible image classification capabilities that extend beyond humans. However, they remain susceptible to image perturbations that a human could not perceive.
Zou, Xukai +5 more
core +1 more source
IEEE AccessAdversarial training remains the most effective empirical defense against adversarial examples, yet it is hindered by the high cost of inner maximization, the clean–robust accuracy trade-off, and the underutilization of information contained in ...
Ahmed Dawod Mohammed Ibrahum +1 more
doaj +1 more source
IEEE Access, 2021 Although the hyperspectral image (HSI) classification has adopted deep neural networks (DNNs) and shown remarkable performances, there is a lack of studies of the adversarial vulnerability for the HSI classifications.
Sungjune Park, Hong Joo Lee, Yong Man Ro
doaj +1 more source
Explainability and Adversarial Robustness for RNNs [PDF]
2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), 2020 Accepted at IEEE BigDataService ...
Alexander Hartl +3 more
openaire +2 more sources
Improving ensemble robustness by collaboratively promoting and demoting adversarial robustness [PDF]
, 2021 Ensemble-based adversarial training is a principled approach to achieve robustness against adversarial attacks. An important technique of this approach is to control the transferability of adversarial examples among ensemble members.
Abraham, Tamas +7 more
core +1 more source
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security
Advanced Materials, EarlyView.An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak +4 more
wiley +1 more source