  The monitoring of individuals/objects has become increasingly possible in
recent years due to the convenience of integrated cameras in many devices. Due
to the important moments or activities of people captured by these devices, it
has made it a great asset for attackers to launch attacks against by exploiting
the weaknesses in these devices. Different studies proposed na\"ive/selective
encryption of the captured visual data for safety but despite the encryption,
an attacker can still access or manipulate such data. This paper proposed a
novel threat model, DEMIS which helps analyse the threats against such
encrypted videos. The paper also examines the attack vectors that can be used
for threats and the mitigation that will reduce or prevent the attack. For
experiments, firstly the data set is generated by applying selective encryption
on the Regions-of-interests (ROI) of the tested videos using the image
segmentation technique and Chacha20 cipher. Secondly, different types of
attacks, such as inverse, lowercase, uppercase, random insertion, and
malleability attacks were simulated in experiments to show the effects of the
attacks, the risk matrix, and the severity of these attacks. Our developed data
set with the original, selective encrypted, and attacked videos are available
on git-repository(https://github.com/Ifeoluwapoo/video-datasets) for future
researchers.
