  Despite the remarkable progress in generative technology, the Janus-faced
issues of intellectual property protection and malicious content supervision
have arisen. Efforts have been paid to manage synthetic images by attributing
them to a set of potential source models. However, the closed-set
classification setting limits the application in real-world scenarios for
handling contents generated by arbitrary models. In this study, we focus on a
challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously
attribute images to known models and identify those from unknown ones. Compared
to existing open-set recognition (OSR) tasks focusing on semantic novelty, OSMA
is more challenging as the distinction between images from known and unknown
models may only lie in visually imperceptible traces. To this end, we propose a
Progressive Open Space Expansion (POSE) solution, which simulates open-set
samples that maintain the same semantics as closed-set samples but embedded
with different imperceptible traces. Guided by a diversity constraint, the open
space is simulated progressively by a set of lightweight augmentation models.
We consider three real-world scenarios and construct an OSMA benchmark dataset,
including unknown models trained with different random seeds, architectures,
and datasets from known ones. Extensive experiments on the dataset demonstrate
POSE is superior to both existing model attribution methods and off-the-shelf
OSR methods.
