Results 121 to 130 of about 7,756 (262)
Bioethics, EarlyView.ABSTRACT Many controversies in medical ethics, particularly those involving conflicts between parents and medical staff over decisions about child patients, are challenging to manage without causing significant polarization and communication issues. This is primarily because the parties involved—parents and physicians—operate at different epistemic ...
Chiara Innorta
wiley +1 more source
IEEE AccessRecent studies have shown that machine-learning models are vulnerable to adversarial attacks. Adversarial attacks are deliberate attempts to modify the input data of a machine learning model in a way that causes it to produce incorrect predictions.
Palakorn Kamnounsing +3 more
doaj +1 more source
POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI
IEEE AccessAdversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee +3 more
doaj +1 more source
Visualizing Image Segmentation Network Behavior Through the Lens of Scale Space Analysis
Computer Graphics Forum, EarlyView.Abstract Deep neural networks are widely used for image segmentation, also in sensitive applications such as medical imaging or autonomous driving. However, few explainable AI methods are available that help developers understand such networks beyond classification.
A. C. Mikliss, T. Schultz
wiley +1 more source
SPINE: VAE‐driven Counterfactuals for Decision Boundary Maps
Computer Graphics Forum, EarlyView.Abstract As Deep Learning models become increasingly complex, Explainable AI becomes essential for deploying machine learning classifiers. Decision Boundary Mapping (DBM) is a technique for visualizing a classifier's global decision boundary. Despite their relative success, current DBM methods rely on global inverse multidimensional projections that ...
I.M. Bloemen, V. Prasad, F. V. Paulovich
wiley +1 more source
Certified Accuracy and Robustness: How different architectures stand up to adversarial attacks
Intelligent Systems with ApplicationsAdversarial attacks are a concern for image classification using neural networks. Numerous methods have been created to minimize the effects of attacks, where the best defense against such attacks is through adversarial training, which has proven to be ...
Azryl Elmy Sarih +2 more
doaj +1 more source
Strictly Conservative Neural Distance Fields
Computer Graphics Forum, EarlyView.Abstract We propose a first method to generate neural unsigned or signed distance fields (SDFs) that are guaranteed to be conservative with respect to a given 3D shape. This means the true distance is never overestimated and the zero‐level set is a bounding volume for the shape. The method makes use of neural network architectures that ensure Lipschitz
I. Ludwig, M. Campen
wiley +1 more source
Adversarial Attacks on Medical Image Classification. [PDF]
Cancers (Basel), 2023 Tsai MJ, Lin PY, Lee ME.
europepmc +1 more source
JCMS: Journal of Common Market Studies, EarlyView.Abstract Strategic and security considerations have become increasingly prominent in trade policy debates, fuelling both liberal and protectionist arguments. However, the extent to which this geopoliticization influences legislative trade attitudes in the European Parliament (EP) remains underexplored.
Lorane Visart
wiley +1 more source
Investigating the Transferability of TOG Adversarial Attacks in YOLO Models in the Maritime Domain
IEEE AccessIn recent years, CNN-based object detectors have been widely adopted in autonomous systems. Although their capabilities are employed across various industries, these detectors are inherently susceptible to adversarial attacks.
Phornphawit Manasut +5 more
doaj +1 more source