Jove
Visualize
Contact Us

Related Concept Videos

Deconvolution01:20

Deconvolution

271
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
271
Neural Circuits01:25

Neural Circuits

1.7K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.7K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

564
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...
564

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Crypto-asset trading on top of Ethereum Blockchain comprehensive dataset.

Scientific data·2025
Same author

XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis.

Journal of imaging informatics in medicine·2025
Same author

Recent Developments in the Theory and Applicability of Swarm Search.

Entropy (Basel, Switzerland)·2023
Same author

Beyond preferential attachment: falling of stars and survival of superstars.

Royal Society open science·2022
Same author

End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware.

Entropy (Basel, Switzerland)·2020
Same author

Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images.

Entropy (Basel, Switzerland)·2020
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Sep 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

DNN Intellectual Property Extraction Using Composite Data.

Itay Mosafi1, Eli Omid David1, Yaniv Altshuler2

  • 1Department of Computer Science, Bar-Ilan University, Ramat-Gan 5290002, Israel.

Entropy (Basel, Switzerland)
|March 25, 2022
PubMed
Summary
This summary is machine-generated.

Deep neural networks are vulnerable to mimicking attacks where a student model steals knowledge from a protected mentor network. Current protection methods, including watermarking, are ineffective against these sophisticated attacks.

Keywords:
adversarial AIartificial intelligencecommunicationcybersecuritydeep learningentropyinformation theorymodelsneural networksswarm intelligence

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.0K
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

665

Related Experiment Videos

Last Updated: Sep 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.0K
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

665

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep neural networks (DNNs) are integral to AI products and services.
  • Intellectual property theft via knowledge extraction from DNNs is a growing concern.
  • Mimicking attacks, using student models to replicate DNN outputs, pose a significant threat.

Purpose of the Study:

  • To introduce a novel method for attacking mentor neural networks using a student model.
  • To demonstrate the effectiveness of this method even without knowledge of the mentor's dataset, architecture, or weights.
  • To assess the vulnerability of DNNs to mimicking attacks and the detectability of student models.

Main Methods:

  • A novel method for generating composite images to attack a mentor neural network.
  • Training a student network to mimic mentor outputs without assumptions on mentor's data, architecture, or weights.
  • Evaluating the student model's accuracy and its imperviousness to watermarking protection.

Main Results:

  • The student model achieved 99% relative accuracy to the protected mentor model on the Cifar-10 test set.
  • The developed student network successfully mimicked the mentor network, stealing significant knowledge.
  • The student model evaded detection by existing watermarking protection methods.

Conclusions:

  • All current neural networks are vulnerable to mimicking attacks, even with basic output.
  • Student models that mimic mentor networks are difficult to detect using current techniques.
  • Existing protection mechanisms against knowledge extraction are insufficient.