Results 81 to 90 of about 7,756 (262)

SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS

Darpan International Research Analysis
In recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research. Along with all the developments comes the threats and security vulnerabilities to Neural Networks and AI models. A few fabricated inputs/samples can lead to deviations in
Akshat Jain, Sanskar Agarwal, Armaan Pareek, Vanshika Singh   +3 more
openaire   +1 more source

Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI‐Integrated Work

Journal of Organizational Behavior, EarlyView.
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts.
Caitlin M. Lancaster, Christopher Flathmann, Jennifer Hsu, Nathan J. McNeese, Thomas A. O'Neill, Eduardo Salas   +5 more
wiley   +1 more source

Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods

Remote Sensing
Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture.
Sumaiya Tasneem, Kazi Aminul Islam
doaj   +1 more source

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani, Rüdiger von Eisenhart‐Rothe, Klaus Woertler, Michael T. Hirschmann, Daniel Rueckert, Florian Hinterwimmer   +5 more
wiley   +1 more source

A Comprehensive Risk Analysis Method for Adversarial Attacks on Biometric Authentication Systems

IEEE Access
Recent threats to deep learning-based biometric authentication systems stem from adversarial attacks exploiting vulnerabilities in deep learning models. While existing studies extensively analyze the risk of such attacks, they primarily focus on isolated
Seong Hee Park, Soo-Hyun Lee, Min Young Lim, Pyo Min Hong, Youn Kyu Lee   +4 more
doaj   +1 more source

Adversarial Vulnerability and Defense in Human Detection: An Experimental Study Using FGSM, PGD, and Adversarial Training on the HERIDAL Dataset [PDF]

International Journal of Innovative Solutions in Engineering
Adversarial attacks pose a serious threat to the reliability of modern artificial intelligence systems, especially in computer vision. Although such attacks rely on very small, often imperceptible perturbations of the input data, they can cause a ...
Marijana Bandić, Maja Kovačić, Fran Pavlović   +2 more
doaj   +1 more source

Harnessing Aggregation‐Induced Emission for Advanced Disease Diagnosis: Mechanisms, Strategies and Future Perspectives

Medicine Bulletin, EarlyView.
The figure was created in BioRender.com. ABSTRACT Accurate and early diagnosis remains a key challenge in modern disease management. Conventional diagnostic methods often suffer from limitations in sensitivity, spatial resolution, and practical usability.
Zizhuo Du, Li Song, Xingrui Chen, Jun Jing, Yingchen Wang, Yue Jiang, Jiahui Gao, Dan Li, Feiran Xie, Peihong Xiao, Ben Zhong Tang   +10 more
wiley   +1 more source

Interdiction Models and Heuristics for Graph Propagation

Networks, EarlyView.
ABSTRACT Given a graph G=(V,E)$$ G=\left(V,E\right) $$ and a set S⊂V$$ S\subset V $$ of activated/infected nodes, we consider the problem of determining the set of c$$ c $$ nodes that minimizes the network propagation on the subgraph that results from the removal of those c$$ c $$ nodes. To measure network propagation, we assume that a node i$$ i $$ is
Agostinho Agra, José Maria Samuco
wiley   +1 more source

Mathematical Analysis of Adversarial Attacks

CoRR, 2018
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM can fool any convolutional neural nets (CNNs) with ReLU activation; the targeted FGSM can mislead any CNNs with ReLU activation to classify any given image into any prescribed
Zehao Dou, Stanley J. Osher, Bao Wang 0001   +2 more
openaire   +2 more sources

The transformative potential of artificial intelligence in pediatric medicine: Current applications, methodological challenges, and future directions

Pediatric Investigation, EarlyView.
Artificial intelligence (AI) offers transformative potential for paediatric diagnosis and treatment, yet implementation faces unique challenges, including data scarcity, algorithmic bias, and children's developmental physiology. This review examines current applications and charts a path toward transparent, equitable, and trustworthy AI in child health.
Ruisong Wang, Xiaoman Ding, Wanyue Zhang, Tieliu Shi   +3 more
wiley   +1 more source