Results 141 to 150 of about 17,780 (281)

Investigating Adversarial Attacks on Deep Learning Models for RGB Remote Sensing Image Classification

reservedIn recent years, there have been significant advancements in the field of Deep Learning and also in that of Remote Sensing (RS) technologies. RS image recognition models based on deep convolution neural networks outperform traditional hand-craft ...
MATTELIGH, ELISA
core  

A “Tech First” Approach to Foreign Policy? The Three Meanings of Tech Diplomacy

Global Policy, EarlyView.
ABSTRACT Scholars have recently argued that international politics is plagued by instability as the world rapidly transitions from one crisis to another. This state of “Permacrisis,” or permanent crises between states, is driven by technological innovations which create new kinds of crises and drive competitions between adversarial states.
Ilan Manor
wiley   +1 more source

Deep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems

Mathematics
Adversarial attacks targeting industrial control systems, such as the Maroochy wastewater system attack and the Stuxnet worm attack, have caused significant damage to related facilities.
Mun-Suk Kim
doaj   +1 more source

PRAT: PRofiling Adversarial aTtacks

, 2023
Intrinsic susceptibility of deep learning to adversarial examples has led to a plethora of attack techniques with a broad common objective of fooling deep models.
Rawat, Yogesh Singh, Ambati, Rahul, Mian, Ajmal, Akhtar, Naveed   +3 more
core  

Western Balkans as the Frontline of Russian Hybrid Warfare

Global Policy, EarlyView.
ABSTRACT Hybrid warfare (HW) scholarship acknowledges the phenomenon's contextual and temporal specificity, yet its dominant conceptual framing has generated a literature largely centred on identifying and categorising hybrid activities. This focus has left the contextual vulnerabilities that enable hybrid threats (HTs) and shape an adversary's ...
Vesna Bojicic‐Dzelilovic
wiley   +1 more source

POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee, Jin-Seoung Kim, Han-Jin Lee, Seok-Hwan Choi   +3 more
doaj   +1 more source

Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved impressive performance in many tasks on graph data. Recent studies show that they are vulnerable to adversarial attacks.
Wang, H, Zhou, C, Wang, J, Li, Z, Wu, J, Yu, PS, Chen, X, Pan, S   +7 more
core   +1 more source

Countering FIMI by Digital Authoritarianisms: Audience Architecture and Reverse Language Engineering

Global Policy, EarlyView.
ABSTRACT Foreign information manipulation and interference (FIMI) campaigns on social media are currently both more accessible and more impactful than the North Atlantic Treaty Organization's (NATO) or European Union's (EU), offering their opponents superiority and efficiency on those platforms.
Michelangelo Conoscenti
wiley   +1 more source

Breaking Shared Perception: Experimental Adversarial Attacks on Cooperative Autonomous Vehicle Systems

reservedCollaborative perception systems help autonomous vehicles see more of their surroundings by sharing information with nearby agents. This teamwork improves awareness and planning, but it also brings new risks: mistakes made by one vehicle can ...
BERNARDI, MARCO
core  

On Adversarial Attacks on Deep Learning Models

, 2019
With recent advancements in the field of artificial intelligence, deep learning has created a niche in the technology space and is being actively used in autonomous and IoT systems globally.
Mani, Nag
core   +1 more source