Skip to content

Algorithm mismatch in spatial steganalysis

Conference/Workshop:
IS&T International Symposium on Electronic Imaging, Media Watermarking, Security, and Forensics 2019
Published: 2019
Primary Author: Stephanie Reinders
Secondary Authors: Li Lin, Yong Guan, Min Wu, Jennifer Newman
Research Area: Digital

The number and availability of stegonographic embedding algorithms continues to grow. Many traditional blind steganalysis frameworks require training examples from every embedding algorithm, but collecting, storing and processing representative examples of each algorithm can quickly become untenable. Our motivation for this paper is to create a straight-forward, nondata-intensive framework for blind steganalysis that only requires examples of cover images and a single embedding algorithm for training. Our blind steganalysis framework addresses the case of algorithm mismatch, where a classifier is trained on one algorithm and tested on another, with four spatial embedding algorithms: LSB matching, MiPOD, S-UNIWARD and WOW.

We use RAW image data from the BOSSbase database and and data collected from six iPhone devices. Ensemble Classifiers with Spatial Rich Model features are trained on a single embedding algorithm and tested on each of the four algorithms. Classifiers trained on MiPOD, S-UNIWARD and WOW data achieve decent error rates when testing on all four algorithms. Most notably, an Ensemble Classifier with an adjusted decision threshold trained on LSB matching data achieves decent detection results on MiPOD, S-UNIWARD and WOW data.

Related Resources

Statistical Methods for the Forensic Analysis of User-Event Data

Statistical Methods for the Forensic Analysis of User-Event Data

A common question in forensic analysis is whether two observed data sets originate from the same source or from different sources. Statistical approaches to addressing this question have been widely…
Hunting wild stego images, a domain adaptation problem in digital image forensics

Hunting wild stego images, a domain adaptation problem in digital image forensics

Digital image forensics is a field encompassing camera identication, forgery detection and steganalysis. Statistical modeling and machine learning have been successfully applied in the academic community of this maturing field.…
Statistical Methods for the Forensic Analysis of Geolocated Event Data

Statistical Methods for the Forensic Analysis of Geolocated Event Data

A common question in forensic analysis is whether two observed data sets originated from the same source or from different sources. Statistical approaches to addressing this question have been widely…
CSAFE 2020 All Hands Meeting

CSAFE 2020 All Hands Meeting

The 2020 All Hands Meeting was held May 12 and 13, 2020 and served as the closing to the last 5 years of CSAFE research and focused on kicking off…
Do you have 44.03 seconds?

44.3 Seconds. That is the average amount of time it takes for a visitor to provide site feedback.
Test it yourself by taking the survey.


A scientist/researcherA member of the forensic science communityA journalist/publicationA studentOther. Please indicate.


Learn more about CSAFE overall.Discover research CSAFE is undertaking.Explore collaboration opportunities.Find tools and education opportunities.Other. Please indicate.


YesNo