  As generative AI progresses rapidly, new synthetic image generators continue
to emerge at a swift pace. Traditional detection methods face two main
challenges in adapting to these generators: the forensic traces of synthetic
images from new techniques can vastly differ from those learned during
training, and access to data for these new generators is often limited. To
address these issues, we introduce the Ensemble of Expert Embedders (E3), a
novel continual learning framework for updating synthetic image detectors. E3
enables the accurate detection of images from newly emerged generators using
minimal training data. Our approach does this by first employing transfer
learning to develop a suite of expert embedders, each specializing in the
forensic traces of a specific generator. Then, all embeddings are jointly
analyzed by an Expert Knowledge Fusion Network to produce accurate and reliable
detection decisions. Our experiments demonstrate that E3 outperforms existing
continual learning methods, including those developed specifically for
synthetic image detection.
