Results 61 to 70 of about 6,370 (239)

AB-VIP: Attack Method Using Brightness Information for Visibility Improvement of Adversarial Perturbations on the NMS Algorithm

IEEE Access
As with classification models, object detection models are vulnerable to adversarial attacks. In particular, adversarial attacks on key components of object detection models such as Region Proposal Network (RPN) and Non-Maximum Suppression (NMS ...
Gwang-Nam Kim, Han-Ju Lee, Han-Jin Lee, Hyeon-Jin Jung, Seok-Hwan Choi   +4 more
doaj   +1 more source

Playing in the Dark: Invisible Chess as a Laboratory for Strategic AI

AI &Innovation, EarlyView.
This paper shows that strategic AI evaluated on perfect‐information benchmarks can be brittle in real adversarial settings. By using invisible chess as a benchmark for hidden state and deception, it argues for stricter testing, human oversight, and more cautious governance of high‐stakes AI systems.
Paolo Ciancarini
wiley   +1 more source

A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

IEEE Access, 2020
Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu, Yu Tian, Ru Zhang, Youqiang Sun, Chan Wang   +4 more
doaj   +1 more source

A Distributed Biased Boundary Attack Method in Black-Box Attack

Applied Sciences, 2021
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang, Jiahui Xu, Wanpeng Zhang, Weidong Wang   +3 more
doaj   +1 more source

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Adversarial attacks on an oblivious recommender [PDF]

Proceedings of the 13th ACM Conference on Recommender Systems, 2019
Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profiles. This paper attempts to reduce this gap.
Konstantina Christakopoulou, Arindam Banerjee 0001   +1 more
openaire   +1 more source

Multi-concept adversarial attacks

CoRR, 2021
As machine learning (ML) techniques are being increasingly used in many applications, their vulnerability to adversarial attacks becomes well-known. Test time attacks, usually launched by adding adversarial noise to test instances, have been shown effective against the deployed ML models.
Vibha Belavadi, Yan Zhou 0001, Murat Kantarcioglu, Bhavani M. Thuraisingham   +3 more
openaire   +2 more sources

From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI

Clinical Pharmacology &Therapeutics, EarlyView.
Drug development productivity has not improved despite five decades of computational advancement, with the probability that a compound entering Phase I achieving regulatory approval remaining near 10%. Each automation wave increased throughput while leaving the interpretive bottleneck intact; scientists continued to formulate questions, evaluate ...
Michael McCoy, Matthew McCoy
wiley   +1 more source

Link Prediction Adversarial Attack

CoRR, 2018
Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep model has also been revealed by carefully designed adversarial examples generated by various adversarial attack methods.
Jinyin Chen, Ziqiang Shi, Yangyang Wu, Xuanheng Xu, Haibin Zheng   +4 more
openaire   +2 more sources

A Review of Artificial Intelligence in Ophthalmology: Key Aspects, Challenges, and Future Directions

Eye &ENT Research, EarlyView.
ABSTRACT Artificial intelligence (AI) is increasingly reshaping ophthalmology because the specialty depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows. This review provides a clinically grounded and translationally oriented synthesis of AI in ophthalmology, covering methodological foundations ...
Partha Pratim Ray
wiley   +1 more source