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

Likelihood ratios for categorical count data with applications in digital forensics

Likelihood ratios for categorical count data with applications in digital forensics

We consider the forensic context in which the goal is to assess whether two sets of observed data came from the same source or from different sources. In particular, we…
CSAFE Project Update & ASCLD FRC Collaboration

CSAFE Project Update & ASCLD FRC Collaboration

This presentation highlighted CSAFE’s collaboration with the ASCLD FRC Collaboration Hub.
Forensic Analysis on Android Social Networking Applications

Forensic Analysis on Android Social Networking Applications

This presentation is from the 75th Anniversary Conference of the American Academy of Forensic Sciences, Orlando, Florida, February 13-18, 2023. Posted with permission of CSAFE.
Source Camera Identification on Multi-Camera Phones

Source Camera Identification on Multi-Camera Phones

Camera identification addresses the scenario where an investigator has a questioned digital image from an unknown camera. The investigator wants to know whether the questioned image was taken by a…