  The classification of forged videos has been a challenge for the past few
years. Deepfake classifiers can now reliably predict whether or not video
frames have been tampered with. However, their performance is tied to both the
dataset used for training and the analyst's computational power. We propose a
deepfake detection method that operates in the latent space of a
state-of-the-art generative adversarial network (GAN) trained on high-quality
face images. The proposed method leverages the structure of the latent space of
StyleGAN to learn a lightweight binary classification model. Experimental
results on standard datasets reveal that the proposed approach outperforms
other state-of-the-art deepfake classification methods, especially in contexts
where the data available to train the models is rare, such as when a new
manipulation method is introduced. To the best of our knowledge, this is the
first study showing the interest of the latent space of StyleGAN for deepfake
classification. Combined with other recent studies on the interpretation and
manipulation of this latent space, we believe that the proposed approach can
further help in developing frugal deepfake classification methods based on
interpretable high-level properties of face images.
