MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation
Abstract
We provide a benchmark for digital Media Forensics Challenge (MFC) evaluations. Our comprehensive data comprises over 176,000 high provenance (HP) images and 11,000 HP videos; more than 100,000 manipulated images and 4,000 manipulated videos; 35 million internet images and 300,000 video clips. We have designed and generated a series of development, evaluation, and challenge datasets, and used them to assess the progress and thoroughly analyze the performance of diverse systems on a variety of media forensics tasks in the past two years. In this paper, we first introduce the objectives, challenges, and approaches to building media forensics evaluation datasets. We then discuss our approaches to forensic dataset collection, annotation, and manipulation, and present the design and infrastructure to effectively and efficiently build the evaluation datasets to support various evaluation tasks. Given a specified query, we build an infrastructure that selects the customized evaluation subsets for the targeted analysis report. Finally, we demonstrate the evaluation results in the past evaluations.
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Popular face recognition systems based on VGG and Facenet are unable to detect deepfakes effectively.
“Test results show that the popular face recog- nition systems based on VGG [65] and Facenet [59, 66] are unable to detect deepfakes effectively.”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [59]
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- Authors
- Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A. N., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., & Fiscus, J.
- Affiliations
- National Institute of Standards and Technology (US)
- Thermo Fisher Scientific (Israel) (IL)
- Venue
- 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)
- Publisher
- IEEE
- Published
- 2019-01
- Pages
- 63-72
- OpenAlex
- W2912308702
- Cited by
- 329
- References
- 31
- Citations by year
- 2026: 48 · 2025: 71 · 2024: 73 · 2023: 48 · 2022: 35 · 2021: 25 · 2020: 20 · 2019: 8 · 2018: 1
- FWCI
- 7.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Keywords
- multimedia forensics, benchmark dataset, manipulated images, manipulated video, image forgery detection, video manipulation detection, data annotation, dataset collection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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