Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Censoring Survival Data01:09

Censoring Survival Data

68
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
68
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

5.9K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
5.9K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

429
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
429
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.7K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.7K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

611
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
611

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Efficient Automated Disease Diagnosis Using Machine Learning Models.

Journal of healthcare engineering·2021
Same author

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images.

Journal of healthcare engineering·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 10, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

683

SecureVision:高级网络安全 深度假冒检测与大数据分析

Naresh Kumar1, Ankit Kundu2

  • 1Maharaja Surajmal Institute of Technology, New Delhi 110058, India.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
概括
此摘要是机器生成的。

SecureVision使用深度学习和大数据分析来检测音频和视觉媒体中复杂的深度假冒. 这种先进的系统通过保护数字完整性免受不断发展的欺骗技术的影响来提高网络安全.

关键词:
音频分析 音频分析大数据分析大数据分析网络安全 网络安全深度学习是一种深度学习.深度假冒检测的检测数字欺骗数字欺骗操纵的内容是操纵的内容.媒体诚信 媒体诚信多模式分析多模式分析多媒体分析分析多媒体分析

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

478
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

相关实验视频

Last Updated: Jun 10, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

683
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

478
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 深度假冒技术通过使复杂的媒体操纵成为可能,对公众的信任和网络安全构成重大威胁.
  • 现有的检测方法与先进的深度假冒技术作斗争,需要新的方法.

研究的目的:

  • 介绍SecureVision,这是一个用于检测深度假冒音频和视觉内容的先进系统.
  • 通过大数据分析和深度学习的新组合,增强改变媒体的检测能力.

主要方法:

  • 利用最先进的深度学习算法进行媒体分析.
  • 实现多模式分析,同时处理音频和视觉数据.
  • 利用大数据分析来实现可扩展性和自我监督学习来实现适应性.

主要成果:

  • 通过多模式分析,表现出更好的深度假冒检测能力.
  • 展示了系统在处理大型数据集的有效性.
  • 验证了SecureVision对先进的深度假冒技术的灵活性和稳定性.

结论:

  • SecureVision提供了一种积极主动,可扩展和高效的防御,以抵御深度假冒威胁.
  • 该系统为数字完整性,隐私和安全建立了新的基准.
  • 多模式分析和自我监督学习是打击不断发展的数字欺骗的关键.