MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation

By Haiying Guan, Mark Kozak, Eric Robertson, Yooyoung Lee, Amy N. Yates, Andrew Delgado, Daniel Zhou, Timothée Kheyrkhah, Jeff M. Smith, Jonathan G. Fiscus. 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2019

318 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/25e5170c178fe2578900fbb49890d98bbf51e46a

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Citations in the corpus

FaceForensics++: Learning to Detect Manipulated Facial Images (2019, background): “The National Institute of Standards and Technology (NIST) released the most extensive dataset for generic image manipulation comprising about 50, 000 forged images (both local and global manipulations) and around 500 forged videos [31].”
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection (2021, methodology): “For the ease of a head-to-head comparison with the state-of-the-art, we adopt CASIAv2 [12] for training and widely used COVER [14], Columbia [21], NIST16 [20], CASIAv1+2 [13] and the more recent IMD [28] for testing.”
Exposing Deep Fakes Using Inconsistent Head Poses (2018, methodology): “The second dataset is a subset from the DARPA MediFor GAN Image/Video Challenge [18] , which has 241 real images and 252 Deep Fake images.”
Media Forensics and DeepFakes: An Overview (2020, background): “NC2017 [208] 2017 various 2667 / 1410 160×120 − 8000×5320 RAW, PNG, BMP, JPG”
Deep Learning for Deepfakes Creation and Detection (2019, methodology): “The second data set comprises 241 real images and 252 deep fake images, which is a subset of data used in the DARPA MediFor GAN Image/Video Challenge [92].”
Deep learning for deepfakes creation and detection: A survey (2019, methodology): “real images and 252 deep fake images, which is a subset of data used in the DARPA MediFor GAN Image/Video Challenge [129].”