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We learn a visual representation that captures information about the camerathat recorded a given photo. To do this, we train a multimodal embeddingbetween image patches and the EXIF metadata that cameras automatically insertinto image files. Our model represents this metadata by simply converting it totext and then processing it with a transformer. The features that we learnsignificantly outperform other self-supervised and supervised features ondownstream image forensics and calibration tasks. In particular, wesuccessfully localize spliced image regions "zero shot" by clustering thevisual embeddings for all of the patches within an image.

EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata