Results 41 to 50 of about 86,248 (260)

Understanding the Energy vs. Adversarial Robustness Trade-Off in Deep Neural Networks

IEEE Open Journal of Circuits and Systems, 2021
Adversarial examples, which are crafted by adding small perturbations to typical inputs in order to fool the prediction of a deep neural network (DNN), pose a threat to security-critical applications, and robustness against adversarial examples is ...
Kyungmi Lee, Anantha P. Chandrakasan
doaj   +1 more source

Outlier Robust Adversarial Training

, 2023
Supervised learning models are challenged by the intrinsic complexities of training data such as outliers and minority subpopulations and intentional attacks at inference time with adversarial samples. While traditional robust learning methods and the recent adversarial training approaches are designed to handle each of the two challenges, to date, no ...
Hu, Shu, Yang, Zhenhuan, Wang, Xin, Ying, Yiming, Lyu, Siwei   +4 more
openaire   +2 more sources

2D Nanomaterials Toward Function‐Ready Superlubricity in Advanced Microsystems

Advanced Materials, EarlyView.
A unified framework links structural and transformation superlubricity with microsystem functions and deployment requirements. Mechanisms, device architectures, integration strategies, AI‐guided discovery, and benchmarking protocols are connected to define function‐ready superlubricity in advanced microsystems.
Yushan Geng, Jun Yang, Yong Yang
wiley   +1 more source

Adversarially Robust Topological Inference

, 2022
The distance function to a compact set plays a crucial role in the paradigm of topological data analysis. In particular, the sublevel sets of the distance function are used in the computation of persistent homology -- a backbone of the topological data analysis pipeline.
Vishwanath, Siddharth, Sriperumbudur, Bharath K., Fukumizu, Kenji, Kuriki, Satoshi   +3 more
openaire   +2 more sources

All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security

Advanced Materials, EarlyView.
An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak, Changgyun Moon, Hyun‐Bin Yu, Sunkook Kim, Dong‐Hwan Kim   +4 more
wiley   +1 more source

Avoiding catastrophic overfitting in fast adversarial training with adaptive similarity step size.

PLoS ONE
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost.
Jie-Chao Zhao, Jin Ding, Yong-Zhi Sun, Ping Tan, Ji-En Ma, You-Tong Fang   +5 more
doaj   +1 more source

PEGAT: Prediction Error-Guided Adversarial Training to Enhance Robustness of Deep Learning Models in Autonomous Vehicles

IEEE Access
Adversarial training is a widely used method to improve the robustness of deep learning models in various applications. Although adversarial training enhances the robustness of the target model, it also suffers from an accuracy versus robustness trade ...
Manzoor Hussain, Zhengyu Shang, Ahmed Dawod Mohammed Ibrahum, Jang-Eui Hong   +3 more
doaj   +1 more source

Understanding adversarial robustness against on-manifold adversarial examples

Pattern Recognition
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One of the hypotheses of the existence of the adversarial examples is the off-manifold assumption: adversarial examples lie off the data manifold. However, recent research showed
Jiancong Xiao, Liusha Yang, Yanbo Fan, Jue Wang, Zhi-Quan Luo   +4 more
openaire   +2 more sources

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Manifold-driven decomposition for adversarial robustness

Frontiers in Computer Science
The adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration.
Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Yi Yao, Mayank Goswami, Chao Chen, Dimitris Metaxas   +6 more
doaj   +1 more source