Results 151 to 160 of about 17,780 (281)

Strategic litigation as a challenge for deliberative democracy

American Journal of Political Science, EarlyView.
Abstract Strategic litigation is a growing public concern, but remains understudied in democratic theory. In strategic litigation, collectives go to court with a political agenda that goes beyond their specific case. How should we assess the legitimacy of strategic litigation? Building on Lafont's model of deliberative democracy and Klein's distinction
Svenja Ahlhaus
wiley   +1 more source

Rigid Body Adversarial Attacks

2025 International Conference on 3D Vision (3DV)
Due to their performance and simplicity, rigid body simulators are often used in applications where the objects of interest can considered very stiff. However, no material has infinite stiffness, which means there are potentially cases where the non-zero compliance of the seemingly rigid object can cause a significant difference between its ...
Aravind Ramakrishnan, David I. W. Levin, Alec Jacobson   +2 more
openaire   +2 more sources

Adversarial Halftone QR Code

IEEE Access
Recent 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, Karin Sumongkayothin, Prarinya Siritanawan, Kazunori Kotani   +3 more
doaj   +1 more source

Adversarial Contrastive Graph Masked AutoEncoder Against Graph Structure and Feature Dual Attacks

Graph Neural Networks (GNNs) have been shown vulnerable to graph adversarial attacks. Current robust graph representation learning methods mainly defend against graph structure attack, and improves performance of GNNs.
Shen, Xiaobo, Pan, Shirui, Shen, X, Shen, W, Shen, Weixuan, Pan, S   +5 more
core   +1 more source

Dealing With Conflicts in Medical Decisions: Epistemic Reasonable Disagreement Between Parents and Medical Staff

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

Adaptive Perturbation for Adversarial Attack

IEEE Transactions on Pattern Analysis and Machine Intelligence
In recent years, the security of deep learning models achieves more and more attentions with the rapid development of neural networks, which are vulnerable to adversarial examples. Almost all existing gradient-based attack methods use the sign function in the generation to meet the requirement of perturbation budget on $L_\infty$ norm. However, we find
Zheng Yuan 0005, Jie Zhang 0071, Zhaoyan Jiang, Liangliang Li, Shiguang Shan   +4 more
openaire   +3 more sources

Certified Accuracy and Robustness: How different architectures stand up to adversarial attacks

Intelligent Systems with Applications
Adversarial 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, Nagender Aneja, Ong Wee Hong   +2 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

Investigating the Transferability of TOG Adversarial Attacks in YOLO Models in the Maritime Domain

IEEE Access
In 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, Md Saleh Ibtasham, Zeynep Yaradanakul, Sepinoud Azimi, Sebastien Lafond, Bogdan Iancu   +5 more
doaj   +1 more source