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 Videos

Steganalysis using image quality metrics.

Ismail Avcibaş1, Nasir Memon, Bülent Sankur

  • 1Dept. of Electron. Eng., Uludag Univ., Bursa, Turkey. avcibas@uludag.edu.tr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
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

Distinguishability of keystroke dynamic template.

PloS one·2022
Same author

Surgical Outcomes of Idiopathic Macular Epiretinal Membrane Peeling.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP·2019
Same author

Cystoid macular oedema after phacoemulsification with and without type 2 diabetes mellitus; a hospital-based clinical prospective trial in Karachi.

JPMA. The Journal of the Pakistan Medical Association·2017
Same author

Adaptive Langevin sampler for separation of t-distribution modelled astrophysical maps.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2010
Same author

3D model retrieval using probability density-based shape descriptors.

IEEE transactions on pattern analysis and machine intelligence·2009
Same author

Bayesian separation of images modeled with MRFs using MCMC.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2009
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

This study introduces novel steganalysis techniques to detect hidden data in images. By analyzing image quality features and using multivariate regression, researchers can accurately identify steganographic modifications.

Area of Science:

  • Digital image forensics
  • Information security
  • Statistical pattern recognition

Background:

  • Steganography conceals data within images, posing security risks.
  • Detecting steganographic content is crucial for digital forensics.
  • Existing steganalysis methods require improvement for accuracy and robustness.

Purpose of the Study:

  • To develop and evaluate new steganalysis techniques for image data.
  • To identify statistical evidence of steganographic algorithms in images.
  • To enhance the detection accuracy of hidden information in digital images.

Main Methods:

  • Utilizing passive and active warden frameworks for steganalysis.
  • Employing image quality metrics identified via analysis of variance (ANOVA).

Related Experiment Videos

  • Building a multivariate regression classifier trained on image quality features and original image estimates.

    • Main Results:

    • Selected image quality metrics effectively distinguish between cover and stego-images.
    • The multivariate regression classifier demonstrates reasonable accuracy in detection.
    • The proposed approach successfully identifies images subjected to steganographic techniques.

    Conclusions:

    • Statistical evidence from image quality features can be exploited for steganalysis.
    • Multivariate regression analysis provides a viable method for detecting steganographic content.
    • The developed techniques offer a promising advancement in digital image security and forensics.