  Active research is going on to securely transmit a secret message or
so-called steganography by using data-hiding techniques in digital images.
After assessing the state-of-the-art research work, we found, most of the
existing solutions are not promising and are ineffective against machine
learning-based steganalysis. In this paper, a lightweight steganography scheme
is presented through graphical key embedding and obfuscation of data through
encryption. By keeping a mindset of industrial applicability, to show the
effectiveness of the proposed scheme, we emphasized mainly deep learning-based
steganalysis. The proposed steganography algorithm containing two schemes
withstands not only statistical pattern recognizers but also machine learning
steganalysis through feature extraction using a well-known pre-trained deep
learning network Xception. We provided a detailed protocol of the algorithm for
different scenarios and implementation details. Furthermore, different
performance metrics are also evaluated with statistical and machine learning
performance analysis. The results were quite impressive with respect to the
state of the arts. We received 2.55% accuracy through statistical steganalysis
and machine learning steganalysis gave maximum of 49.93~50% correctly
classified instances in good condition.
