  Videos have become a powerful tool for spreading illegal content such as
military propaganda, revenge porn, or bullying through social networks. To
counter these illegal activities, it has become essential to try new methods to
verify the origin of videos from these platforms. However, collecting datasets
large enough to train neural networks for this task has become difficult
because of the privacy regulations that have been enacted in recent years. To
mitigate this limitation, in this work we propose two different solutions based
on transfer learning and multitask learning to determine whether a video has
been uploaded from or downloaded to a specific social platform through the use
of shared features with images trained on the same task. By transferring
features from the shallowest to the deepest levels of the network from the
image task to videos, we measure the amount of information shared between these
two tasks. Then, we introduce a model based on multitask learning, which learns
from both tasks simultaneously. The promising experimental results show, in
particular, the effectiveness of the multitask approach. According to our
knowledge, this is the first work that addresses the problem of social media
platform identification of videos through the use of shared features.
