Results 31 to 40 of about 3,240,231 (313)
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
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
Christina Göpfert +2 more
openaire +4 more sources
Delving into the Adversarial Robustness of Federated Learning [PDF]
AAAI Conference on Artificial Intelligence, 2023 In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largely unexplored.
J Zhang +6 more
semanticscholar +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 +3 more sources
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better [PDF]
IEEE International Conference on Computer Vision, 2021 Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger ...
Bojia Zi +3 more
semanticscholar +1 more source
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT [PDF]
, 2021 As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan +13 more
core +1 more source
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles [PDF]
Computer Vision and Pattern Recognition, 2022 Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness of trajectory prediction or investigated whether the worst-case prediction ...
Qingzhao Zhang +4 more
semanticscholar +1 more source
A Robust Adversarial Example Attack Based on Video Augmentation
Applied Sciences, 2023 Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin +3 more
doaj +1 more source
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System [PDF]
Computers & security, 2023 Deep learning based intrusion detection systems (DL-based IDS) have emerged as one of the best choices for providing security solutions against various network intrusion attacks.
Xin Yuan +5 more
semanticscholar +1 more source
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
semanticscholar +1 more source