Results 101 to 110 of about 5,384,791 (287)

Adversarial attacks on fingerprint liveness detection

EURASIP Journal on Image and Video Processing, 2020
Deep neural networks are vulnerable to adversarial samples, posing potential threats to the applications deployed with deep learning models in practical conditions.
Jianwei Fei, Zhihua Xia, Peipeng Yu, Fengjun Xiao   +3 more
doaj   +1 more source

Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials

Advanced Science, EarlyView.
Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Zikang Guo, Yunwen Wu, Yuhui Guo, Shenghong Ju   +3 more
wiley   +1 more source

Detecting Adversarial Examples in Convolutional Neural Networks

CoRR, 2018
The great success of convolutional neural networks has caused a massive spread of the use of such models in a large variety of Computer Vision applications. However, these models are vulnerable to certain inputs, the adversarial examples, which although are not easily perceived by humans, they can lead a neural network to produce faulty results.
Stefanos Pertigkiozoglou, Petros Maragos
openaire   +2 more sources

Physics Informed Generative Surrogate Learning for Full Wave Validated Real Time Beamforming in Programmable Metasurfaces

Advanced Electronic Materials, EarlyView.
A physics‐guided generative surrogate framework is developed for programmable metasurface beamforming. Mode‐conditioned binary state generation, aperture‐physics prediction, routed residual correction, NSGA‐II optimization, and CST validation are combined to support fast candidate screening and full‐wave beam refinement across single‐beam, dual‐beam ...
Wenqian Liu, Weidong Chen, Chang Chen, Xiang Zhang, Sensong An   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Masked Language Model Based Textual Adversarial Example Detection [PDF]

Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown
Zhang, Yanjun, Zhong, Qi, Hu, Shengshan, Zheng, Xufei, Zhang, Xiaomei, Zhang, Zhaoxi, Zhang, Leo Yu   +6 more
core   +1 more source

Adversarial Attacks in Computer Vision: A Survey [PDF]

Jisuanji gongcheng
Deep learning has driven the development of artificial intelligence, which is widely used in computer vision. It provides breakthroughs and remarkable results in complex tasks such as image recognition, object detection, object tracking, and face ...
QIN Yingxin, ZHANG Kejia, PAN Haiwei, JU Yahao
doaj   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Understanding Measures of Uncertainty for Adversarial Example Detection

CoRR, 2018
Measuring uncertainty is a promising technique for detecting adversarial examples, crafted inputs on which the model predicts an incorrect class with high confidence. But many measures of uncertainty exist, including predictive en- tropy and mutual information, each capturing different types of uncertainty.
Lewis Smith, Yarin Gal
openaire   +3 more sources