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

Observational Learning01:12

Observational Learning

210
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
210
Associative Learning01:27

Associative Learning

444
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
444
Classification of Systems-II01:31

Classification of Systems-II

177
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
177
Classification of Systems-I01:26

Classification of Systems-I

215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
129
Machines: Problem Solving II01:30

Machines: Problem Solving II

335
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
335

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

Efficacy of Botulinum Toxin Injections for Erectile Dysfunction and Premature Ejaculation: A Meta-Analysis of Randomized Controlled Trials.

Current drug research reviews·2026
Same author

PREVALENCE AND RISK FACTORS OF UROLITHIASIS AMONG THE POPULATION OF AL-BAHA REGION, SAUDI ARABIA.

Georgian medical news·2025
Same author

The Impact of Ureteral Access Sheaths on Radiation Exposure in the Ureterorenoscopic Treatment of Urolithiasis.

Urologia internationalis·2024
Same author

Plasma Homocysteine Levels and Cardiovascular Events in Patients With End-Stage Renal Disease: A Systematic Review.

Cureus·2023
Same author

Technical Outcome, Clinical Success, and Complications of Low-Milliampere Computed Tomography Fluoroscopy-Guided Drainage of Lymphoceles Following Radical Prostatectomy with Pelvic Lymph Node Dissection.

Diagnostics (Basel, Switzerland)·2022
Same author

Clinical Evaluation of Single-Use, Fiber-Optic, and Digital Ureterorenoscopes in the Treatment of Kidney Stones.

Urologia internationalis·2022

相关实验视频

Updated: Jul 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

623

基于二级经典机器学习验证方法的对抗意识深度学习系统.

Mohammed Alkhowaiter1,2, Hisham Kholidy3, Mnassar A Alyami1

  • 1College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

经典的机器学习模型对对抗性攻击具有免疫力. 我们提出了一个新的深度学习系统,使用经典模型进行强大的图像分类,优于目前的防御.

关键词:
具有对抗性的机器学习.计算机安全 计算机安全深度神经网络是一个神经网络.图像取证医学 图像取证医学图像操纵检测检测 图像操纵检测

更多相关视频

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

3.8K
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

571

相关实验视频

Last Updated: Jul 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

623
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

3.8K
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

571

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 深度学习模型在图像分类方面表现出色,但容易受到对抗性攻击.
  • 敌对攻击利用了深度学习模型固有的神经网络结构.
  • 经典的机器学习模型,如随机森林,缺乏神经网络设计,表明潜在的免疫力.

研究的目的:

  • 调查经典机器学习模型对对抗性攻击的脆弱性.
  • 为增强图像分类安全性提出一种新的对抗意识深度学习系统.
  • 评估拟议系统对最先进的对抗防御方法的有效性.

主要方法:

  • 经典机器学习模型对流行的对抗性攻击的实验性评估.
  • 开发一种混合深度学习系统,将经典机器学习模型作为二次验证层.
  • 在CIFAR-100数据集上测试拟议的系统.

主要成果:

  • 经典的机器学习模型证明了对敌对攻击的免疫力,支持了最初的假设.
  • 提出的对抗意识深度学习系统通过输出不匹配有效地检测到对抗攻击.
  • 与现有的最先进的对抗防御系统相比,混合系统实现了更高的性能.

结论:

  • 经典的机器学习模型提供了强大的防御对抗攻击,由于他们的非神经网络架构.
  • 在深度学习中将古典模型作为验证系统集成到深度学习中,可以提高对抗性强度,而不会损害初级模型的准确性.
  • 拟议的对抗意识系统为安全可靠的图像分类提供了一个有希望的方向.