Localized Multi-Object Watermarking

As images are increasingly reused and recomposed, tracing the provenance of individual objects requires watermarks that remain recoverable after partial theft. Beyond conventional global image watermarking, localized watermarking has recently emerged to protect individual objects within an image. However, localized watermarks remain vulnerable to object-level partial theft, where cropping, transformation, and recomposition of a watermarked object may preserve only a small portion of the embedded signal. Furthermore, the problem becomes more challenging when a composite image contains multiple objects, requiring independent message extraction and localization. To address these challenges, we propose MoMark, a localized multi-object watermarking framework that embeds independent messages into localized regions and jointly recovers their messages and spatial support. We employ a two-stage training strategy: single-object message extraction and localization, followed by multi-object training with partial-support objectives. At inference, MoMark pools messages over local patches and separates or merges watermarked regions by message similarity, without requiring ground-truth masks. Evaluated on complex segmentation masks with multiple independently watermarked objects, MoMark improves object-level message extraction and localization across geometric distortions, non-geometric distortions, and a variety of partial-theft settings.