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 Experiment Video

Updated: Jun 21, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

AGASI: A Generative Adversarial Network-Based Approach to Strengthening Adversarial Image Steganography.

Haiju Fan1,2, Changyuan Jin1,2, Ming Li1,2

  • 1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Entropy (Basel, Switzerland)
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

HAG-NET: Hiding Data and Adversarial Attacking with Generative Adversarial Network.

Entropy (Basel, Switzerland)·2024
Same author

Cryptanalysis of an Image Encryption Algorithm Based on Random Walk and Hyperchaotic Systems.

Entropy (Basel, Switzerland)·2022
Same author

Cryptanalysis of a New Chaotic Image Encryption Technique Based on Multiple Discrete Dynamical Maps.

Entropy (Basel, Switzerland)·2021
Same author

Lsr2 of Mycobacterium tuberculosis is a DNA-bridging protein.

Nucleic acids research·2008
Same author

Amphetamine selectively enhances avoidance responding to a less salient stimulus in rats.

Journal of neural transmission (Vienna, Austria : 1996)·2008
Same author

Intrarenal antigens activate CD4+ cells via co-stimulatory signals from dendritic cells.

Journal of the American Society of Nephrology : JASN·2008

We developed AGASI, a generative adversarial network (GAN) approach to improve image steganography robustness. This method enhances secret image privacy against advanced steganalysis by increasing misclassification rates in detection models.

Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Steganography is vital for image privacy protection.
  • Advanced steganalysis, particularly deep learning models, threatens traditional steganography.
  • Existing methods struggle to maintain image quality and robustness against detection.

Purpose of the Study:

  • To propose AGASI, a Generative Adversarial Network (GAN)-based approach to enhance adversarial image steganography.
  • To improve the robustness of stego-images against sophisticated steganalysis techniques.
  • To maintain high-quality secret image extraction while increasing resistance to detection.

Main Methods:

  • Utilized an encoder as a generator and a discriminator to form a GAN for adversarial training.
Keywords:
adversarial attacksgenerative adversarial network (GAN)information securitysteganography

Related Experiment Videos

Last Updated: Jun 21, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

  • Implemented a GAN framework to minimize the difference between original and extracted secret images.
  • Employed a decoder for effective secret image extraction from stego-images.

    • Main Results:

    • AGASI ensures high-quality secret images and effective extraction.
    • The method significantly reduces the accuracy of neural network steganalysis classifiers, inducing misclassifications.
    • Achieved an 84.73% misclassification rate under PGD attack, a 23.31% increase over comparable methods.
    • Demonstrated increased embedding capacity in the steganography system.

    Conclusions:

    • AGASI provides a robust solution for image privacy protection against advanced steganalysis.
    • The GAN-based approach effectively balances stego-image quality, detection evasion, and embedding capacity.
    • AGASI represents a significant advancement in secure steganography techniques.