  With the advent of personalized generation models, users can more readily
create images resembling existing content, heightening the risk of violating
portrait rights and intellectual property (IP). Traditional post-hoc detection
and source-tracing methods for AI-generated content (AIGC) employ proactive
watermark approaches; however, these are less effective against personalized
generation models. Moreover, attribution techniques for AIGC rely on passive
detection but often struggle to differentiate AIGC from authentic images,
presenting a substantial challenge. Integrating these two processes into a
cohesive framework not only meets the practical demands for protection and
forensics but also improves the effectiveness of attribution tasks. Inspired by
this insight, we propose a unified approach for image copyright source-tracing
and attribution, introducing an innovative watermarking-attribution method that
blends proactive and passive strategies. We embed copyright watermarks into
protected images and train a watermark decoder to retrieve copyright
information from the outputs of personalized models, using this watermark as an
initial step for confirming if an image is AIGC-generated. To pinpoint specific
generation techniques, we utilize powerful visual backbone networks for
classification. Additionally, we implement an incremental learning strategy to
adeptly attribute new personalized models without losing prior knowledge,
thereby enhancing the model's adaptability to novel generation methods. We have
conducted experiments using various celebrity portrait series sourced online,
and the results affirm the efficacy of our method in source-tracing and
attribution tasks, as well as its robustness against knowledge forgetting.
