4 research outputs found
Tackling Android Stego Apps in the Wild
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. We develop algorithms and tools for
signature detection on stego apps, and provide solutions to issues encountered
when creating ML classifiers
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. Still, large gaps exist between academic results and applications used by practicing forensic analysts, especially when the target samples are drawn from a different population than the data in a reference database.
This thesis contains four published papers aiming at narrowing this gap in three different fields: mobile stego app detection, digital image steganalysis and camera identification. It is the first work to explore a way of extending the academic methods to real world images created by apps. New ideas and methods are developed for target images with very rich flexibility in the embedding rates, embedding algorithms, exposure settings and camera sources. The experimental results proved that the proposed methods work very well, even for the devices which are not included in the reference database