Results 81 to 90 of about 447,373 (304)

Approximating High-Order Adversarial Attacks Using Runge−Kutta Methods

Tsinghua Science and Technology
Adversarial attacks craft adversarial examples (AEs) to fool convolution neural networks. The mainstream gradient-based attacks, based on first-order optimization methods, encounter bottlenecks to generate high transferable AEs attacking unknown models ...
Anjie Peng, Guoqiang Shi, Zhi Lin, Hui Zeng, Xing Yang   +4 more
doaj   +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

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

Boosting Adversarial Attacks with Momentum [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed.
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su 0006, Jun Zhu 0001, Xiaolin Hu 0001, Jianguo Li   +6 more
openaire   +2 more sources

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

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  

Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks

, 2022
In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants
Michael Blumenstein, Ying Zhao, Di Wu, Yu, Shui, Zhang, Jiale, Jiale Zhang, Chen, Junjun, Shui Yu, Wu, Di, Junjun Chen, Zhao, Ying, Blumenstein, Michael   +11 more
core   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

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