Results 11 to 20 of about 3,240,231 (313)
Machine Learning and Knowledge ExtractionAdversarial training, a widely used technique for fortifying the robustness of machine learning models, has seen its effectiveness further bolstered by modifying loss functions or incorporating additional terms into the training objective.
Sander Joos +4 more
doaj +4 more sources
Recent Advances in Adversarial Training for Adversarial Robustness [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically.
Tao Bai +4 more
semanticscholar +5 more sources
Towards Adversarial Robustness via Feature Matching [PDF]
IEEE Access, 2020 Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification.
Zhuorong Li +4 more
doaj +2 more sources
On Saliency Maps and Adversarial Robustness [PDF]
, 2021 A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustness against adversaries. Works have shown that adversarially trained models exhibit more interpretable saliency maps than their non-robust counterparts, and that this behavior
Puneet Mangla +2 more
openaire +5 more sources
Adversarial Robustness of Neural Networks from the Perspective of Lipschitz Calculus: A Survey
ACM Computing SurveysWe survey the adversarial robustness of neural networks from the perspective of Lipschitz calculus in a unifying fashion by expressing models, attacks and safety guarantees—that is, a notion of measurable trustworthiness—in a mathematical language. After
Monty-Maximilian Zühlke, Daniel Kudenko
exaly +2 more sources
Rethinking data augmentation for adversarial robustness [PDF]
Information SciencesRecent work has proposed novel data augmentation methods to improve the adversarial robustness of deep neural networks. In this paper, we re-evaluate such methods through the lens of different metrics that characterize the augmented manifold, finding ...
Hamid Eghbalzadeh +7 more
semanticscholar +2 more sources
Assessing the adversarial robustness of multimodal medical AI systems: insights into vulnerabilities and modality interactions [PDF]
Frontiers in MedicineThe emergence of both task-specific single-modality models and general-purpose multimodal large models presents new opportunities, but also introduces challenges, particularly regarding adversarial attacks.
Ekaterina Mozhegova +5 more
doaj +2 more sources
Artificial Intelligence ReviewThe current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs.
Shaker El-Sappagh
exaly +2 more sources
Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving. [PDF]
Sensors (Basel), 2023 Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks.
Mahima KTY +3 more
europepmc +2 more sources
Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks [PDF]
Computer Methods and Programs in Biomedicine, 2023 Liang Liang
exaly +2 more sources