The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
RAISE: a raw images dataset for digital image forensics
Abstract
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.
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Cites
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A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
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An Evaluation of Popular Copy-Move Forgery Detection Approaches
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Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency
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An Overview on Image Forensics
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The 'Dresden Image Database' for benchmarking digital image forensics
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Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499
Do GANs leave artificial fingerprints?
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2019) · 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00103 · Cited by 361
Fast Image Processing with Fully-Convolutional Networks
Chen, Q., Xu, J., & Koltun, V. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.273 · Cited by 341
Metadata
- Authors
- Dang-Nguyen, D.-T., Pasquini, C., Conotter, V., & Boato, G.
- Venue
- Proceedings of the 6th ACM Multimedia Systems Conference
- Publisher
- ACM
- Published
- 2015-03-18
- Pages
- 219-224
- OpenAlex
- W2055745001
- Cited by
- 508
- References
- 12
- FWCI
- 12.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Dang-Nguyen, D.-T., Pasquini, C., Conotter, V., & Boato, G. (2015). RAISE: a raw images dataset for digital image forensics. Proceedings of the 6th ACM Multimedia Systems Conference. https://doi.org/10.1145/2713168.2713194
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