Results 61 to 70 of about 992 (214)

Universal attention guided adversarial defense using feature pyramid and non-local mechanisms

Scientific Reports
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, significantly hindering the development of deep learning technologies in high-security domains. A key challenge is that current defense methods often lack universality,
Jiawei Zhao, Lizhe Xie, Siqi Gu, Zihan Qin, Yuning Zhang, Zheng Wang, Yining Hu   +6 more
doaj   +1 more source

The Ecological U‐Turn in Nordic Forestry: Policy Learning, Measurement, and the Retrenchment of Ecological Ambitions

Environmental Policy and Governance, EarlyView.
ABSTRACT Since the 1990s, environmental policy has shifted toward ecosystem protection and biodiversity conservation, a development often described as an ecological turn. This article argues that in practice this turn has taken the form of an ecological “U‐turn,” as environmental ambitions are subordinated to production‐oriented objectives.
Gisle Andersen, Monika Berg, Hogne Lerøy Sataøen   +2 more
wiley   +1 more source

Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense

Applied Sciences
Escalating advancements in artificial intelligence (AI) has prompted significant security concerns, especially with its increasing commercialization. This necessitates research on safety measures to securely utilize AI models.
Inpyo Hong, Sokjoon Lee
doaj   +1 more source

Knowledge sourcing, geopolitics, and FDI: An empirical analysis on the US green and digital sectors

Global Strategy Journal, EarlyView.
Abstract Research Summary This paper examines how foreign direct investment (FDI) shapes firms' sourcing of knowledge in the digital and green domains under rising geopolitical frictions. We assemble a firm–country dyadic panel (2013–2020) linking US patent backward citations to firms' FDI, enriched with bilateral geopolitical distance and host‐country
Alberto Maria Radici
wiley   +1 more source

Adversarial Backdoor Defense in CLIP

CoRR
Multimodal contrastive pretraining, exemplified by models like CLIP, has been found to be vulnerable to backdoor attacks. While current backdoor defense methods primarily employ conventional data augmentation to create augmented samples aimed at feature alignment, these methods fail to capture the distinct features of backdoor samples, resulting in ...
Junhao Kuang, Siyuan Liang 0004, Jiawei Liang, Kuanrong Liu, Xiaochun Cao   +4 more
openaire   +2 more sources

Artificial intelligence and liquidation: Reality, destiny and fantasy

International Insolvency Review, EarlyView.
Abstract Artificial intelligence (AI) is increasingly reshaping the administration of corporate liquidation. Beyond its established role in financial prediction and data analytics, AI is now assisting insolvency practitioners in identifying the onset of financial distress, managing creditor communications, tracing and valuing assets and enhancing ...
Kai Zhang, Jingchen Zhao
wiley   +1 more source

A divide-and-conquer reconstruction method for defending against adversarial example attacks

Visual Intelligence
In recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However,
Xiyao Liu, Jiaxin Hu, Qingying Yang, Ming Jiang, Jianbiao He, Hui Fang   +5 more
doaj   +1 more source

An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems

Scientific Reports
Machine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity.
Zeinab Awad, Magdy Zakaria, Rasha Hassan
doaj   +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

Adversarial defenses via vector quantization

Neurocomputing
This is the author-accepted version of our paper published in Neurocomputing.
Zhiyi Dong, Yongyi Mao
openaire   +2 more sources