Results 101 to 110 of about 12,832 (282)

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

Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems

, 2020
Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data ...
Vähäkainu, Petri, Lehto, Martti, Kariluoto, Antti   +2 more
core  

Adversarial Attack for Asynchronous Event-Based Data

, 2022
Deep neural networks (DNNs) are vulnerable to adversarial examples that are carefully designed to cause the deep learning model to make mistakes. Adversarial examples of 2D images and 3D point clouds have been extensively studied, but studies on event ...
Myung, Hyun, Lee, Wooju
core   +1 more source

Navigating the Rapids: How Non‐Governmental Organization Managers Develop Strategic Adaptation to Repressive Political Environments

Public Administration and Development, EarlyView.
ABSTRACT This article explores the management adaptation strategies non‐governmental organizations (NGOs) managers employ in order to operate in repressive political environments. It answers the question: how do NGO managers initiate, manage and sustain internal change when the political/regulatory environment changes?
Charles Kaye‐Essien, Shahjahan Bhuiyan, Ghadeer Ali   +2 more
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

Why do we burn? Examining arguments underpinning the use of prescribed burning to manage wildfire risk

People and Nature, EarlyView.
Abstract Managing wildfire risk requires consideration of complex and uncertain scientific evidence as well as trade‐offs between different values and goals. Conflicting perspectives on what values and goals are most important, what ought to be done and what trade‐offs are acceptable complicate those decisions.
Pele J. Cannon, Sarah Clement
wiley   +1 more source

Ctta: a novel chain-of-thought transfer adversarial attacks framework for large language models

Cybersecurity
Recent studies have indicated that large language models (LLMs) remain susceptible to adversarial attacks, despite enhanced robustness through the chain-of-thought (CoT) capability.
Xinxin Yue, Zhiyong Zhang, Junchang Jing, Weiguo Wang   +3 more
doaj   +1 more source

Distillation-Based Cross-Model Transferable Adversarial Attack for Remote Sensing Image Classification

Remote Sensing
Deep neural networks have achieved remarkable performance in remote sensing image (RSI) classification tasks. However, they remain vulnerable to adversarial attack.
Xiyu Peng, Jingyi Zhou, Xiaofeng Wu
doaj   +1 more source

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

PANDA: Practical Adversarial Attack Against Network Intrusion Detection

While adversarial machine learning (AML) attacks have become prevalent in the computer vision (CV) domain, their applications in other domains, such as network intrusion detection systems (NIDS), remain limited.
Kumar, V, Kim, DD, Swain, SK, Bai, G
core   +1 more source