  The rapid evolvement of deepfake creation technologies is seriously threating
media information trustworthiness. The consequences impacting targeted
individuals and institutions can be dire. In this work, we study the evolutions
of deep learning architectures, particularly CNNs and Transformers. We
identified eight promising deep learning architectures, designed and developed
our deepfake detection models and conducted experiments over well-established
deepfake datasets. These datasets included the latest second and third
generation deepfake datasets. We evaluated the effectiveness of our developed
single model detectors in deepfake detection and cross datasets evaluations. We
achieved 88.74%, 99.53%, 97.68%, 99.73% and 92.02% accuracy and 99.95%, 100%,
99.88%, 99.99% and 97.61% AUC, in the detection of FF++ 2020, Google DFD,
Celeb-DF, Deeper Forensics and DFDC deepfakes, respectively. We also identified
and showed the unique strengths of CNNs and Transformers models and analysed
the observed relationships among the different deepfake datasets, to aid future
developments in this area.
