Results 171 to 180 of about 5,380,268 (331)
A Universal Detection Method for Adversarial Examples and Fake Images. [PDF]
Sensors (Basel), 2022 Lai J, Huo Y, Hou R, Wang X.
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Surreptitious Adversarial Examples through Functioning QR Code. [PDF]
J Imaging, 2022 Chindaudom A +3 more
europepmc +1 more source
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Advanced Intelligent Discovery, EarlyView.Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns. [PDF]
J Imaging, 2022 Zong W, Chow YW, Susilo W, Kim J, Le NT.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Universal adversarial examples and perturbations for quantum classifiers. [PDF]
Natl Sci Rev, 2022 Gong W, Deng DL.
europepmc +1 more source
Adversarial Examples in Machine Learning
, 2018 Deep neural networks have been recently achieving high accuracy on many important tasks, most notably image classification. However, these models are not robust to slightly perturbed inputs known as adversarial examples.
Kocián, Matěj
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Measuring the Robustness of Neural Networks via Minimal Adversarial Examples [PDF]
, 2017 Neural networks are highly sensitive to adversarial examples, which cause large output deviations with only small input perturbations. However, little is known quantitatively about the distribution and prevalence of such adversarial examples.
Gao, Sicun +3 more
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AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking
, 2023 Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to characterize the feature uniqueness of adversarial examples, or to distinguish DNN ...
Chen, Ruoxi +3 more
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