EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata
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Abstract (arXiv)
We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions "zero shot" by clustering the visual embeddings for all of the patches within an image.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 35 of its 86 references that are in the map. First posted on arXiv 11 Jan 2023.
Builds on
35 of 86 references.
References · 35
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Citing papers per year: 2023: 2; 2024: 5; 2025: 9; 2026: 1.
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12 most cited of 14 All 14 (JSON)
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- 2024PhotoHolmes: a Python library for forgery detection in digital images19 Dec 2024
- 2024Scale-adaptive UAV Geo-localization via Height-aware Partition Learning16 Dec 2024
- 2024CoAPT: Context Attribute words for Prompt Tuning18 Jul 2024
- 2024Binding Touch to Everything: Learning Unified Multimodal Tactile Representations31 Jan 2024
12 newest of 14 All 14 (JSON)