Results 81 to 90 of about 17,780 (281)

Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy

Epilepsia, EarlyView.
Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A. Sanchez, Rachel J. Smith, Shruti H. Agashe, Jorge A. Gonzalez‐Martinez, Jennifer Hopp, Adam S. Charles, Sridevi V. Sarma, Joon‐Yi Kang   +10 more
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

Adversarial Attacks against the Perception System of Autonomous Vehicles

, 2023
The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety.
Gao, Yuxing (author)
core  

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack

CoRR
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These techniques are primarily executed at the sample level, frequently overlooking the intrinsic parameters of models.
Zhibo Jin, Jiayu Zhang 0001, Zhiyu Zhu, Chenyu Zhang, Jiahao Huang, Jianlong Zhou, Fang Chen 0001   +6 more
openaire   +2 more sources

Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors

Mathematics
Adaptive adversarial attacks, where adversaries tailor their strategies with full knowledge of defense mechanisms, pose significant challenges to the robustness of adversarial detectors. In this paper, we introduce RADAR (Robust Adversarial Detection via
Raz Lapid, Almog Dubin, Moshe Sipper
doaj   +1 more source

Study on Adversarial Sample Attacks on Deep Learning Based Fingerprinting Indoor Localization [PDF]

Jisuanji gongcheng
This study investigated adversarial attacks on Deep Learning(DL) based Wi-Fi fingerprint indoor positioning systems, which have significantly improved indoor localization performance by effectively extracting deep features from Received Signal Strength ...
ZHANG Xuejun, XI Ayou, JIA Xiaohong, ZHANG Bin, LI Mei, DU Xiaogang, HUANG Haiyan
doaj   +1 more source

A Review of Overcurrent Protection in Smart Grids Under Cyber‐Physical Threats With a Cyber‐Physical Evaluation Framework

Energy Science &Engineering, EarlyView.
By manipulating current and voltage measurements, an assailant can induce unwanted relay action while attempting to avoid detection. Detecting advanced cyber intrusions in power protection environments requires specialised data analysis and anomaly detection methods.
Feras Alasali, Anas AlMajali, Naser Naily, Hassen Loukil, Salah AbuGhalyon, William Holderbaum, Abdelaziz Salah Saidi   +6 more
wiley   +1 more source

A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

Future Internet
With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem.
Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, Naima Kaabouch   +3 more
doaj   +1 more source

Adversarial Vulnerability and Defense in Human Detection: An Experimental Study Using FGSM, PGD, and Adversarial Training on the HERIDAL Dataset [PDF]

International Journal of Innovative Solutions in Engineering
Adversarial attacks pose a serious threat to the reliability of modern artificial intelligence systems, especially in computer vision. Although such attacks rely on very small, often imperceptible perturbations of the input data, they can cause a ...
Marijana Bandić, Maja Kovačić, Fran Pavlović   +2 more
doaj   +1 more source