RAISE - A Raw Images Dataset for Digital Image Forensics

Author: University of Cagliari

Partner: No

Contact: Duc-Tien Dang-Nguyen (ductien.dangnguyen@diee.unica.it)

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Total: 8156

Description

Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.

Access

The files are available for download via HTTP. Link: http://mmlab.science.unitn.it/RAISE/

Broken link!

The original link was: http://mmlab.science.unitn.it/RAISE/

References and Citation

Use of the datasets in published work should be acknowledged by a full citation to the authors' papers [DPC15] at the MMSys conference (Proceedings of ACM MMSys '15, Portland, Oregon, March 18-20, 2015).

References

  • DPC15: D. Dang-Nguyen, C. Pasquini, V. Conotter, G. Boato. RAISE - A Raw Images Dataset for Digital Image Forensics, Proceedings of ACM MMSys '15, Portland, Oregon, March 18-20, 2015.