Quality-Agnostic Deepfake Detection with Intra-model Collaborative
  Learning
Binh M. Le Simon S. Woo
Deepfake has recently raised a plethora of societal concerns over its
possible security threats and dissemination of fake information. Much research
on deepfake detection has been undertaken. However, detecting low quality as
well as simultaneously detecting different qualities of deepfakes still remains
a grave challenge. Most SOTA approaches are limited by using a single specific
model for detecting certain deepfake video quality type. When constructing
multiple models with prior information about video quality, this kind of
strategy incurs significant computational cost, as well as model and training
data overhead. Further, it cannot be scalable and practical to deploy in
real-world settings. In this work, we propose a universal intra-model
collaborative learning framework to enable the effective and simultaneous
detection of different quality of deepfakes. That is, our approach is the
quality-agnostic deepfake detection method, dubbed QAD . In particular, by
observing the upper bound of general error expectation, we maximize the
dependency between intermediate representations of images from different
quality levels via Hilbert-Schmidt Independence Criterion. In addition, an
Adversarial Weight Perturbation module is carefully devised to enable the model
to be more robust against image corruption while boosting the overall model's
performance. Extensive experiments over seven popular deepfake datasets
demonstrate the superiority of our QAD model over prior SOTA benchmarks.