  Recent advancements in Artificial Intelligence namely in Deep Learning has
heightened its adoption in many applications. Some are playing important roles
to the extent that we are heavily dependent on them for our livelihood.
However, as with all technologies, there are vulnerabilities that malicious
actors could exploit. A form of exploitation is to turn these technologies,
intended for good, to become dual-purposed instruments to support deviant acts
like malicious software trojans. As part of proactive defense, researchers are
proactively identifying such vulnerabilities so that protective measures could
be developed subsequently. This research explores a novel blackbox trojanising
approach using a simple network structure modification to any deep learning
image classification model that would transform a benign model into a deviant
one with a simple manipulation of the weights to induce specific types of
errors. Propositions to protect the occurrence of such simple exploits are
discussed in this research. This research highlights the importance of
providing sufficient safeguards to these models so that the intended good of AI
innovation and adoption may be protected.
