  Steganography algorithms facilitate communication between a source and a
destination in a secret manner. This is done by embedding messages/text/data
into images without impacting the appearance of the resultant images/videos.
Steganalysis is the science of determining if an image has secret messages
embedded/hidden in it. Because there are numerous steganography algorithms, and
since each one of them requires a different type of steganalysis, the
steganalysis process is extremely challenging. Thus, researchers aim to develop
one universal steganalysis to detect all known and unknown steganography
algorithms, ideally in real-time. Universal steganalysis extracts a large
number of features to distinguish stego images from cover images. However, the
increase in features leads to the problem of the curse of dimensionality (CoD),
which is considered to be an NP-hard problem. This COD problem additionally
makes real-time steganalysis hard. A large number of features generates large
datasets for which machine learning cannot generate an optimal model.
Generating a machine learning based model also takes a long time which makes
real-time processing appear impossible in any optimization for time-intensive
fields such as visual computing. Possible solutions for CoD are deep learning
and evolutionary algorithms that overcome the machine learning limitations. In
this study, we investigate previously developed evolutionary algorithms for
boosting real-time image processing and argue that they provide the most
promising solutions for the CoD problem.
