  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.
