Open Set Synthetic Image Source Attribution
Shengbang Fang Tai D. Nguyen Matthew C. Stamm
AI-generated images have become increasingly realistic and have garnered
significant public attention. While synthetic images are intriguing due to
their realism, they also pose an important misinformation threat. To address
this new threat, researchers have developed multiple algorithms to detect
synthetic images and identify their source generators. However, most existing
source attribution techniques are designed to operate in a closed-set scenario,
i.e. they can only be used to discriminate between known image generators. By
contrast, new image-generation techniques are rapidly emerging. To contend with
this, there is a great need for open-set source attribution techniques that can
identify when synthetic images have originated from new, unseen generators. To
address this problem, we propose a new metric learning-based approach. Our
technique works by learning transferrable embeddings capable of discriminating
between generators, even when they are not seen during training. An image is
first assigned to a candidate generator, then is accepted or rejected based on
its distance in the embedding space from known generators' learned reference
points. Importantly, we identify that initializing our source attribution
embedding network by pretraining it on image camera identification can improve
our embeddings' transferability. Through a series of experiments, we
demonstrate our approach's ability to attribute the source of synthetic images
in open-set scenarios.