  Recent breakthroughs in generative modeling have sparked interest in
practical single-model attribution. Such methods predict whether a sample was
generated by a specific generator or not, for instance, to prove intellectual
property theft. However, previous works are either limited to the closed-world
setting or require undesirable changes to the generative model. We address
these shortcomings by, first, viewing single-model attribution through the lens
of anomaly detection. Arising from this change of perspective, we propose
FLIPAD, a new approach for single-model attribution in the open-world setting
based on final-layer inversion and anomaly detection. We show that the utilized
final-layer inversion can be reduced to a convex lasso optimization problem,
making our approach theoretically sound and computationally efficient. The
theoretical findings are accompanied by an experimental study demonstrating the
effectiveness of our approach and its flexibility to various domains.
