Results 71 to 80 of about 447,373 (304)
Adversarial Risk Análysis for Counterterrorism Modelling [PDF]
, 2013 Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
core
Hidden Conditional Adversarial Attacks
, 2022 Deep neural networks are vulnerable to maliciously crafted inputs called adversarial examples. Research on unprecedented adversarial attacks is significant since it can help strengthen the reliability of neural networks by alarming potential threats ...
Byun, JunYoung +3 more
core +1 more source
Advanced Intelligent Systems, EarlyView.Secure Fusion‐X harmonizes unstructured NVD descriptions with structured CVSS/CWE/CPE metadata via decision‐level fusion, overcoming the fragility of traditional unimodal models. Automated assessment of software vulnerability exploitability is essential for intelligent cyber defense, yet its effectiveness is often hindered by unstable, delayed, or ...
Mona Dolati +3 more
wiley +1 more source
Generalization of Defense Effects Learned from a Single Adversarial Attack
ComputationAdversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the
Dongxian Niu, Lin Shi
doaj +1 more source
A Robust Method to Protect Text Classification Models against Adversarial Attacks
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Text classification is one of the main tasks in natural language processing. Recently, adversarial attacks have shown a substantial negative impact on neural network-based text classification models. There are few defenses to strengthen model predictions
BALA MALLIKARJUNARAO GARLAPATI +2 more
doaj +1 more source
Real-Time Adversarial Attacks [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can ...
Yuan Gong 0001 +3 more
openaire +4 more sources
Bridging the translational gap in clinical nanomedicine: From rational design to clinical reality
BMEMat, EarlyView.Nanotechnology offers a multi‐faceted approach to modern healthcare, encompassing high‐sensitivity diagnostic imaging, innovative vaccine delivery, and targeted therapeutics. This review explores how these nanomedicine applications address critical challenges in cancer recurrence and the treatment of neurologic and respiratory diseases to combat rising
Ahmed H. Ghonaim +9 more
wiley +1 more source
Научно-технический вестник информационных технологий, механики и оптикиModern artificial intelligence (AI) technologies are being used in a variety of fields, from science to everyday life. However, the widespread use of AI-based systems has highlighted a problem with their vulnerability to adversarial attacks.
A. A. Vorobeva +4 more
doaj +1 more source
Eye &ENT Research, EarlyView.ABSTRACT Artificial intelligence (AI) is reshaping ophthalmology from task‐specific image analysis toward multimodal, longitudinal, and clinically integrated decision support. This narrative review summarizes the methodological evolution of ophthalmic AI, including traditional machine learning, task‐specific deep learning, self‐supervised learning ...
Yuxin Liu, Hanruo Liu
wiley +1 more source
ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks
Existing self-supervised adversarial training (self-AT) methods rely on hand-crafted adversarial attack strategies for PGD attacks, which fail to adapt to the evolving learning dynamics of the model and do not account for instance-specific ...
Saini, Rajkumar +5 more
core +2 more sources