  The rapid development of technologies and artificial intelligence makes
deepfakes an increasingly sophisticated and challenging-to-identify technique.
To ensure the accuracy of information and control misinformation and mass
manipulation, it is of paramount importance to discover and develop artificial
intelligence models that enable the generic detection of forged videos. This
work aims to address the detection of deepfakes across various existing
datasets in a scenario with limited computing resources. The goal is to analyze
the applicability of different deep learning techniques under these
restrictions and explore possible approaches to enhance their efficiency.
