Results 21 to 30 of about 2,268,403 (339)
Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications [PDF]
IEEE Journal on Selected Areas in Communications, 2023 End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of
Gu Nan +9 more
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Towards quantum enhanced adversarial robustness in machine learning [PDF]
Nature Machine Intelligence, 2023 Machine learning algorithms are powerful tools for data-driven tasks such as image classification and feature detection. However, their vulnerability to adversarial examples—input samples manipulated to fool the algorithm—remains a serious challenge. The
Maxwell T. West +7 more
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Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach [PDF]
Neural Information Processing Systems, 2023 Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived from diverse neural flows, concentrating on their connection to various ...
Kai Zhao +5 more
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Feature Separation and Recalibration for Adversarial Robustness [PDF]
Computer Vision and Pattern Recognition, 2023 Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions.
Woo Jae Kim +3 more
semanticscholar +1 more source
Intelligent Systems with Applications, 2023 Deep neural networks, despite their remarkable success in various language understanding tasks, have been found vulnerable to adversarial attacks and subtle input perturbations, revealing a robustness shortfall.
Jingliang Fang +5 more
doaj +1 more source
Feature Denoising for Improving Adversarial Robustness [PDF]
Computer Vision and Pattern Recognition, 2018 Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these ...
Cihang Xie +4 more
semanticscholar +1 more source
Adversarial robustness of amortized Bayesian inference [PDF]
International Conference on Machine Learning, 2023 Bayesian inference usually requires running potentially costly inference procedures separately for every new observation. In contrast, the idea of amortized Bayesian inference is to initially invest computational cost in training an inference network on ...
Manuel Glöckler +2 more
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Language-Driven Anchors for Zero-Shot Adversarial Robustness [PDF]
Computer Vision and Pattern Recognition, 2023 Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly fo-cus on improving adversarial robustness in the fully super-vised setting, leaving the challenging domain of zero-shot adversarial robustness an ...
Xiao Li +5 more
semanticscholar +1 more source
On the Adversarial Robustness of Camera-based 3D Object Detection [PDF]
Trans. Mach. Learn. Res., 2023 In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost.
Shaoyuan Xie +3 more
semanticscholar +1 more source
Adversarial Robustness Curves [PDF]
, 2020 The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems. This uncertainty has, in turn, lead to considerable research effort in understanding adversarial robustness. In this work, we take first steps towards separating robustness analysis from the
Göpfert, Christina +2 more
openaire +2 more sources