Campus Units

Mathematics

Document Type

Conference Proceeding

Conference

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Publication Version

Accepted Manuscript

Link to Published Version

http://dx.doi.org/10.23919/APSIPA.2018.8659525

Publication Date

11-2018

First Page

18493986

DOI

10.23919/APSIPA.2018.8659525

Conference Date

November 12-15, 2018

City

Honolulu, HI

Abstract

Digital image forensics is a young but maturing field, encompassing key areas such as camera identification, detection of forged images, and steganalysis. However, large gaps exist between academic results and applications used by practicing forensic analysts. To move academic discoveries closer to real-world implementations, it is important to use data that represent “in the wild” scenarios. For detection of stego images created from steganography apps, images generated from those apps are ideal to use. In this paper, we present our work to perform steg detection on images from mobile apps using two different approaches: “signature” detection, and machine learning methods. A principal challenge of the ML task is to create a great many of stego images from different apps with certain embedding rates. One of our main contributions is a procedure for generating a large image database by using Android emulators and reverse engineering techniques, the first time ever done. We develop algorithms and tools for signature detection on stego apps, and provide solutions to issues encountered when creating ML classifiers.

Comments

This is an accepted manuscript published as Chen, Wenhao, Li Lin, Min Wu, and Jennifer Newman. "Tackling android stego apps in the wild." In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1564-1573. IEEE, 2018. Posted with permission of CSAFE.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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