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Images tell powerful stories but cannot always be trusted. Matching imagesback to trusted sources (attribution) enables users to make a more informedjudgment of the images they encounter online. We propose a robust image hashingalgorithm to perform such matching. Our hash is sensitive to manipulation ofsubtle, salient visual details that can substantially change the story told byan image. Yet the hash is invariant to benign transformations (changes inquality, codecs, sizes, shapes, etc.) experienced by images during onlineredistribution. Our key contribution is OSCAR-Net (Object-centric Scene GraphAttention for Image Attribution Network); a robust image hashing model inspiredby recent successes of Transformers in the visual domain. OSCAR-Net constructsa scene graph representation that attends to fine-grained changes of everyobject's visual appearance and their spatial relationships. The network istrained via contrastive learning on a dataset of original and manipulatedimages yielding a state of the art image hash for content fingerprinting thatscales to millions of images.

OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution