Results 71 to 80 of about 148,046 (305)
Adversarial Attacks against the Perception System of Autonomous Vehicles
, 2023 The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety.
Gao, Yuxing (author)
core
Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 more
wiley +1 more source
DEEP DETECTOR HEALTH MANAGEMENT UNDER ADVERSARIAL CAMPAIGNS
International Journal of Prognostics and Health Management, 2019 Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious ...
Javier Echauz +6 more
doaj +1 more source
IEEE Access, 2022 An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety.
Seok-Hwan Choi +3 more
doaj +1 more source
CoRR, 2019 Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks. Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy. Through finding the best policy in well-designed search space of data augmentation, AutoAugment can significantly improve ...
Xinyu Zhang +3 more
openaire +4 more sources
Defending Against Adversarial Attacks with Camera Image Pipelines
, 2023 Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these models into making a false prediction on an image that was correctly predicted without the ...
Zhang, Yuxuan
core
Adversarial attacks and adversarial robustness in computational pathology. [PDF]
, 2022 Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks.
van Treeck, Marko +13 more
core +2 more sources
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Adversarial guided diffusion models for adversarial purification
Neural NetworksDiffusion model (DM) based adversarial purification (AP) has proven to be a powerful defense method that can remove adversarial perturbations and generate a purified example without threats. In principle, the pre-trained DMs can only ensure that purified examples conform to the same distribution of the training data, but it may inadvertently compromise
Guang Lin 0002 +4 more
openaire +4 more sources
The Disagreement Power of an Adversary [PDF]
Distributed Computing, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carole Delporte-Gallet +3 more
openaire +3 more sources