Results 121 to 130 of about 31,109 (263)

Rethinking adversarial attacks on neuromorphic models

Neuromorphic Computing and Engineering
Spiking neural networks (SNN) are biologically inspired artificial neural networks that emulate the behaviour of biological neurons in spiking-based computational units.
Soukaina Aji, Pierre Boulet, Mohamed Bouassida, Ihsen Alouani   +3 more
doaj   +1 more source

Vision transformers: the threat of realistic adversarial patches

Artificial Intelligence for Security and Defence Applications III
The increasing reliance on machine learning systems has made their security a critical concern. Evasion attacks enable adversaries to manipulate the decision-making processes of AI systems, potentially causing security breaches or misclassification of targets. Vision Transformers (ViTs) have gained significant traction in modern machine learning due to
Cools, Kasper, Maathuis, Clara, van Oers, Alexander M., Hübner, Claudia S., Deligiannis, Nikos, Vandewal, Marijke, De Cubber, Geert   +6 more
openaire   +2 more sources

Xstainer: A Novel Virtual Staining Tool Powered by Advanced Deep Learning Techniques

Advanced Intelligent Systems, EarlyView.
Xstainer is a deep learning–based virtual staining framework that converts hematoxylin and eosin‐stained whole slide images into multiple histochemical stains, including Masson's trichrome, Periodic acid‐Schiff, Jones methenamine silver, and Toluidine blue.
Fatma Nur Kinali, Derya Demir, Kutsev B. Ozyoruk, Yasin Almalioglu, Emre C. Kose, Drew F. K. Williamson, Gurdeniz Serin, G. Evren Keles, Banu S. Kumbaraci, Ceren Utku, Uguray P. Hacisalihoglu, Ilknur Turkmen, Figen Ozturk, Kayhan Basak, Yavuz Nuri Ertas, Mehmet Turan   +15 more
wiley   +1 more source

OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection

IEEE Access
In the evolving landscape of deep neural network security, adversarial patch attacks present a serious challenge for object detection systems. We introduce OD-Shield, a novel defense approach that employs a convolutional autoencoder framework to detect ...
Byeongchan Kim, Heemin Kim, Minjung Kang, Hyunjee Nam, Sunghwan Park, Jaewoo Lee, Il-Youp Kwak   +6 more
doaj   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

A Texture-Guided Adversarial Defense Framework Against Deepfake Generation

IEEE Access
Deepfakes are an increasing danger to the reliability of digital media, with several detection algorithms developed to identify manipulated content. Although detection approaches continue to improve, proactive defenses that protect images by embedding ...
Maryam Bukhari, Muazzam Maqsood, Asma Sattar, Mi Young Lee   +3 more
doaj   +1 more source

Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches

Advanced Physics Research, EarlyView.
Two methods for the optimization of chiral reflection by a metamaterial made of either GaP/Air or PMMA/Air interfaces are compared, showing approaches towards fast design exploration and high‐performance results: a neural‐network pipeline and a genetic algorithm. The structures considered are characterized by a periodic, chiral texturation with a shape
Davide Filippozzi, Alexandre Mayer, Nicolas Roy, Wei Fang, Arash Rahimi‐Iman   +4 more
wiley   +1 more source

Prioritizing Feasible and Impactful Actions to Enable Secure AI Development and Use in Biology

Biotechnology and Bioengineering, EarlyView.
ABSTRACT As artificial intelligence continues to enhance biological innovation, the potential for misuse must be addressed to fully unlock the potential societal benefits. While significant work has been done to evaluate general‐purpose AI and specialized biological design tools (BDTs) for biothreat creation risks, actionable steps to mitigate the risk
Josh Dettman, Emily Lathrop, Aurelia Attal‐Juncqua, Matthew Nicotra, Allison Berke   +4 more
wiley   +1 more source

Prompt-Guided Environmentally Consistent Adversarial Patch

CoRR
Adversarial attacks in the physical world pose a significant threat to the security of vision-based systems, such as facial recognition and autonomous driving. Existing adversarial patch methods primarily focus on improving attack performance, but they often produce patches that are easily detectable by humans and struggle to achieve environmental ...
Chaoqun Li, Huanqian Yan, Lifeng Zhou, Tairan Chen, Zhuodong Liu, Hang Su   +5 more
openaire   +2 more sources

When Do Robots Go Green? Unveiling Mechanisms, Thresholds, and Spillovers of Industrial Robotics on Global Ecological Capacity

Business Strategy and the Environment, EarlyView.
ABSTRACT This paper examines the relationship between industrial robotics adoption and ecological capacity, measured by biocapacity, using panel data from 50 countries over the period 2000–2024. We investigate the transmission mechanisms, non‐linearities, spatial spillovers, and heterogeneity characterizing this relationship.
Brahim Bergougui, Hamid Ghazi H. Sulimany   +1 more
wiley   +1 more source