  Modern deepfake detectors have achieved encouraging results, when training
and test images are drawn from the same data collection. However, when these
detectors are applied to images produced with unknown deepfake-generation
techniques, considerable performance degradations are commonly observed. In
this paper, we propose a novel deepfake detector, called SeeABLE, that
formalizes the detection problem as a (one-class) out-of-distribution detection
task and generalizes better to unseen deepfakes. Specifically, SeeABLE first
generates local image perturbations (referred to as soft-discrepancies) and
then pushes the perturbed faces towards predefined prototypes using a novel
regression-based bounded contrastive loss. To strengthen the generalization
performance of SeeABLE to unknown deepfake types, we generate a rich set of
soft discrepancies and train the detector: (i) to localize, which part of the
face was modified, and (ii) to identify the alteration type. To demonstrate the
capabilities of SeeABLE, we perform rigorous experiments on several widely-used
deepfake datasets and show that our model convincingly outperforms competing
state-of-the-art detectors, while exhibiting highly encouraging generalization
capabilities.
