Dataset Viewer
Auto-converted to Parquet Duplicate
Search is not available for this dataset
image
image
label
class label
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
0D01
End of preview. Expand in Data Studio

VISION — mirror

This repository redistributes the VISION dataset. It is a mirror for research and engineering convenience. All credit belongs to the original authors; nothing here is original work by the redistributor.

Attribution (required by the licence)

Shullani, D., Fontani, M., Iuliani, M., Al Shaya, O., & Piva, A. (2017). VISION: a video and image dataset for source identification. EURASIP Journal on Information Security, 2017(1), 15. https://doi.org/10.1186/s13635-017-0067-2

Original source: https://lesc.dinfo.unifi.it/VISION/ Produced by the Image and Communication Laboratory (LESC), Department of Information Engineering, University of Florence.

Licence

Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) https://creativecommons.org/licenses/by-sa/4.0/

Verified against the upstream project page and the EURASIP paper on 2026-08-21.

What that means for anyone using this mirror:

  • Attribution — you must credit Shullani et al. as above, and indicate if you made changes.
  • ShareAlike — if you distribute an adapted version of this material, that adaptation must also be CC BY-SA 4.0. ShareAlike triggers on distribution, not on internal use: training on it privately does not oblige you to publish anything.
  • No additional restrictions — you may not apply legal or technological measures that restrict others from doing anything the licence permits.

Whether trained model weights constitute Adapted Material under CC BY-SA is legally untested. The mainstream reading is that they do not. This mirror takes no position and offers no legal advice; if it matters to your use, take advice.

Contents

Camera-native images and videos from 35 devices, including native captures and platform-recompressed variants (WhatsApp, YouTube, Facebook). Its value is precisely that it is native-capture material: real EXIF, real quantisation tables, real device diversity — the properties that publisher-resized corpora such as Open Images do not have.

Why this mirror exists

It is the out-of-domain evaluation set for a forensic detection project. Downloading from the upstream host is slow from some regions; this mirror exists so evaluation runs are reproducible. If the upstream terms change, this mirror should be removed — please open a discussion if you are one of the authors and would prefer it taken down.

Corrections

  • 2026-08-21 — README, licence declaration and attribution added. This repository was previously public with no licence, no attribution and no README, which did not satisfy CC BY-SA 4.0's conditions for redistribution. That was an oversight by the redistributor, corrected on discovery.
Downloads last month
165