StegoDB: A Statistically-designed 

Image Dataset for Benchmarking 

Steganalysis Algorithms

Jennifer Newman*, PI

Yong Guan**, Co-PI

Min Wu#, Co-PI

Stephanie Reinders and Li Lin, PhD students*

*Mathematics Department, Iowa State University, Ames IA

**Electrical & Computer Engineering Department, Iowa State University, 

Ames IA

#Department of Electrical and Computer Engineering, and Institute for 

Advanced Computer Studies, University of Maryland, College Park 
Forensics@NIST 11/8/2016

WeHide app

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Alice sends a stego image to Bob

Eve is spying

Extract

HERE ARE THE 

ANSWERS TO ALL 

OF THE HOMEWORK 

QUESTIONS!!!
Forensics@NIST 11/8/2016

WeHide app

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Question: If you are Eve, 

how do you determine if 

an image has a hidden 

message?

Eve is spying

Alice sends a stego image to Bob
Forensics@NIST 11/8/2016

Overview

• What is steganography and steganalysis?

• Motivation for creating a database for steganalysis

• Why our work is novel

• What is in our dataset

• Project update

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Forensics@NIST 11/8/2016

What is steganography and steganalysis?

• Steganography: hiding a message (payload) inside a digital 

photo by changing its pixel values in a visually

imperceptible way

• However, it may be statistically or otherwise detectable

• Cover image: an image that has no payload 

• Stego image: an image that contains an embedded payload 

• Steganalysis: classifying an image as stego or cover, 

typically using a statistical learning methods (SVM, 

linear classifiers, etc.) and a particular image dataset

• We do not focus on extraction or decryption of any payload

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Forensics@NIST 11/8/2016

Pattern Classifiers for Steganalysis

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Figure from Schaathun, H.G.: Machine learning in image steganalysis. Wiley, pp. 26 (2012).

• Training Objects: data used for producing Model

• Features are a statistical representation of an image

• The set of images gives rise to a set of values that the classifier uses to distinguish 

between cover and stego

• The more information known about the training data, the better you can design 

your steganalysis experiments within a statistical framework

• TRAINING DATASET IS VERY IMPORTANT!!!
Forensics@NIST 11/8/2016

Why a standard dataset for steganalysis?

• Steganalysis researchers have noted that the type of images used for 

training and testing a classifier can have a important effect on the 

detection rate[2,3] . PMD = probability of Missed Detection (of stego); 

 PFA = probability of False Alarm (cover identified as stego). 

• There is no standard dataset of images used for steganalysis allowing 

different experiments to be conducted that takes into account the 

different sources of the image data used for training the classifiers

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RAW images

JPEG images

Scanned 

images

Trained 

classifier

Trained 

classifier

Trained 

classifier

Type (PMD+PFA )/2

RAW 16.6%

JPEG 1.7%

Scanned 22.8%

[2] Fridrich, J.: Stegnaography in Digital Media: Principles, Algorithms, and ApplicationsWiley, pp. 26 (2010).

[3] Sedighi, V., J. Fridrich, and R. Cogranne. "Toss that BOSSbase, Alice!." IS&T intl symposium on Electronic Imaging. 2016.

(ave. detection 

error)
Forensics@NIST 11/8/2016

StegoDB: Guiding principles

• Our database follows these principles

• Data are copyright-free and publicly available at no cost.

• Authentication of the origin/pedigree of each image.

• Collection of a minimum list of specific information on each 

image in the database.

• Collect data in such a way so that experiments using StegoDB 

image data are repeatable.

• The novelty of our dataset is that the data is collected in 

this principled way, unlike other current stego datasets

• Initially we collect data from mobile phones and their 

apps

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Forensics@NIST 11/8/2016

Project goals

• Design data collection process, what data items to be 

collected, and what additional parameters to add to database

• Collect data

• Use NoSQL database MongoDB to store data

• Easy to use; convenient to maintain; extends to hold very large 

amounts of data; flexible to add information in the future;

• Other databases are stored in organized folders; hard to query 

based on “image size” for example 

• Add additional parameters to records, including parameters 

produced by processing image data

• Evaluate the data

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Forensics@NIST 11/8/2016

What data we collect

• Previous researchers note these aspects of training data can 

affect detection results:

• Image size; previous processing; image content; added noise; 

image format; saturation level; compression level

• These all can influence detection error rate

• We use this information to guide what image data we collect 

and what additional data we will store in our database, so that 

we will have a wider range of known parameters represented 

by the image data

• We will also collect as much other information as possible 

(meta data; ??) so that it may be possible to use these 

parameters in future experiments should there be other 

influences identified later

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Forensics@NIST 11/8/2016

Information our records hold

• We collect phone information; image type; app(s) used; 

meta data; scene type; 

• We will compute and/or identify in image data:

• saturation levels; 

• double compression/multiple compression if JPEG; 

• any known pre-processing methods; 

• scene information (outdoor, indoor), if available

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Image: cover1.jpg Phone: phone2 Type: iPhone6sPlus [exif] sat=5%

Image: cover2.jpg Phone: phone4 Type: iPhone6s [exif] sat=9%

Image: cover3.jpg Phone: phone1 Type: iPhone6s [exif] sat=4%

Image: cover4.jpg Phone: phone3 Type: iPhone7 [exif] sat=5%

Image: stego1.jpg Phone: phone2 Type: iPhone6sPlus Cover: Cover1.jpg App: WeHide

Image: stego2.jpg Phone: phone2 Type: iPhone6sPlus Cover: Cover1.jpg App: WeHide

Image: stego3.jpg Phone: phone4 Type: iPhone6s Cover: Cover2.jpg App: Cloak

Image: Stego4.jpg Phone: phone4 Type: iPhone6s Cover: Cover2.jpg App: Cloak
Forensics@NIST 11/8/2016

Questions?

• Thank you!

• Question to audience:

• Since knowledge of the target users and software is 

important, we are looking for a partner to collaborate with 

to help identify issues that forensic crime labs or forensic 

research labs have with using steganalysis

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