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Media Forensics Challenge Image Provenance Evaluation and State-of-the-Art Analysis on Large-Scale Benchmark Datasets

Published

Author(s)

Xiongnan Jin, Yooyoung Lee, Jonathan G. Fiscus, Haiying Guan, Amy Yates, Andrew Delgado, Daniel F. Zhou

Abstract

With the development of storage, transmission, editing, and sharing tools, digital forgery images are propagating rapidly. The need for image provenance analysis has never been more timely. Typical applications are content tracking, copyright enforcement, and forensics reasoning. However, large-scale image provenance datasets, which contain diverse manipulation history graphs with various manipulation operations and rich metadata, are still needed to facilitate the research. It is one of the major factors that hinders the development of techniques for image provenance analysis. To address this issue, we introduce large-scale benchmark datasets for provenance analysis, namely Media Forensics Challenge-Provenance (MFC-Prov) datasets. Two provenance tasks are designed along with evaluation metrics. Furthermore, extensive analysis is conducted for system performance in terms of accuracy on our datasets.
Citation
NIST Interagency/Internal Report (NISTIR) - 8325
Report Number
8325

Keywords

Media Forensics Challenge, Provenance Evaluation, MFC-Prov, Benchmark Dataset, Image Provenance Analysis.

Citation

Jin, X. , Lee, Y. , , J. , Guan, H. , Yates, A. , Delgado, A. and Zhou, D. (2020), Media Forensics Challenge Image Provenance Evaluation and State-of-the-Art Analysis on Large-Scale Benchmark Datasets, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8325 (Accessed December 8, 2022)
Created October 25, 2020, Updated June 24, 2021