  GAN-generated deepfakes as a genre of digital images are gaining ground as
both catalysts of artistic expression and malicious forms of deception,
therefore demanding systems to enforce and accredit their ethical use. Existing
techniques for the source attribution of synthetic images identify subtle
intrinsic fingerprints using multiclass classification neural nets limited in
functionality and scalability. Hence, we redefine the deepfake detection and
source attribution problems as a series of related binary classification tasks.
We leverage transfer learning to rapidly adapt forgery detection networks for
multiple independent attribution problems, by proposing a semi-decentralized
modular design to solve them simultaneously and efficiently. Class activation
mapping is also demonstrated as an effective means of feature localization for
model interpretation. Our models are determined via experimentation to be
competitive with current benchmarks, and capable of decent performance on human
portraits in ideal conditions. Decentralized fingerprint-based attribution is
found to retain validity in the presence of novel sources, but is more
susceptible to type II errors that intensify with image perturbations and
attributive uncertainty. We describe both our conceptual framework and model
prototypes for further enhancement when investigating the technical limits of
reactive deepfake attribution.
