  On the basis of DefakeHop, an enhanced lightweight Deepfake detector called
DefakeHop++ is proposed in this work. The improvements lie in two areas. First,
DefakeHop examines three facial regions (i.e., two eyes and mouth) while
DefakeHop++ includes eight more landmarks for broader coverage. Second, for
discriminant features selection, DefakeHop uses an unsupervised approach while
DefakeHop++ adopts a more effective approach with supervision, called the
Discriminant Feature Test (DFT). In DefakeHop++, rich spatial and spectral
features are first derived from facial regions and landmarks automatically.
Then, DFT is used to select a subset of discriminant features for classifier
training. As compared with MobileNet v3 (a lightweight CNN model of 1.5M
parameters targeting at mobile applications), DefakeHop++ has a model of 238K
parameters, which is 16% of MobileNet v3. Furthermore, DefakeHop++ outperforms
MobileNet v3 in Deepfake image detection performance in a weakly-supervised
setting.
