Jove
Visualize
Contact Us
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 Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

185
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
185
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

117
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
117
Lossless Lines01:23

Lossless Lines

164
In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi,...
164
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

292
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
292
Neural Circuits01:25

Neural Circuits

1.4K
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...
1.4K
Inverting and Non-inverting OpAmps01:20

Inverting and Non-inverting OpAmps

863
In an inverting amplifier, the input voltage is connected through a resistor to the inverting terminal. Meanwhile, the non-inverting terminal is grounded and a feedback resistor is established between the inverting and output terminal, as depicted in Figure 1.
863

You might also read

Related Articles

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

Sort by
Same author

Albumin as a "Trojan Horse" for polymeric nanoconjugate transendothelial transport across tumor vasculatures for improved cancer targeting.

Biomaterials science·2018
Same author

Dysregulated Response of Follicular Helper T Cells to Hepatitis B Surface Antigen Promotes HBV Persistence in Mice and Associates With Outcomes of Patients.

Gastroenterology·2018
Same author

Clinicopathological Features to Predict Progression of IgA Nephropathy with Mild Proteinuria.

Kidney & blood pressure research·2018
Same author

Gut Microbiome Composition Predicts Infection Risk During Chemotherapy in Children With Acute Lymphoblastic Leukemia.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2018
Same author

Risk of bias and methodological issues in randomised controlled trials of acupuncture for knee osteoarthritis: a cross-sectional study.

BMJ open·2018
Same author

Pursuing sustainable productivity with millions of smallholder farmers.

Nature·2018

Related Experiment Video

Updated: Aug 16, 2025

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

603

Lossless Image Steganography Based on Invertible Neural Networks.

Lianshan Liu1, Li Tang1, Weimin Zheng1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Entropy (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces a novel image steganography method using invertible neural networks. The technique ensures high visual quality and security while achieving perfect secret information recovery.

Keywords:
deep learninginvertible neural networkslossless recoverysteganography

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Aug 16, 2025

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

603
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Traditional image steganography methods often prioritize visual similarity over secret information recovery accuracy.
  • Existing techniques may compromise the integrity of hidden data during the steganography process.

Purpose of the Study:

  • To propose an image steganography method utilizing invertible neural networks for enhanced invisibility, security, and lossless data recovery.
  • To introduce a mapping module for compressing embedded information, improving stego-image quality and anti-detection capabilities.

Main Methods:

  • Developed a steganography scheme based on invertible neural networks (INNs).
  • Integrated a mapping module to compress secret information before embedding.
  • Converted secret information into a binary sequence for embedding via INN forward operations.
  • Recovered secret information using the inverse operations of the INNs.

Main Results:

  • Achieved high invisibility and security in stego images.
  • Demonstrated lossless recovery of secret information.
  • Improved stego-image quality and anti-detection performance through information compression.
  • Experimental results showed competitive performance in visual quality and security.

Conclusions:

  • The proposed invertible neural network-based image steganography method offers superior performance in terms of invisibility, security, and data recovery.
  • The integration of a mapping module further enhances stego-image quality and resistance to detection.
  • The method guarantees 100% accuracy in secret information extraction, addressing a key limitation of prior approaches.