CoMoFoD — New database for copy-move forgery detection

By Dijana Tralic, Ivan Zupancic, Sonja Grgic, Mislav Grgic. Proceedings Elmar, 2013

391 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7f37998a72b7475afad32e2e58e02d3eb721b5ac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Luisa Verdoliva
2. Yue Wu
3. Premkumar Natarajan
4. Wael Abd-Almageed
5. Haiying Guan
6. Mark Kozak
7. Eric Robertson
8. Yooyoung Lee

Citations in the corpus

• Media Forensics and DeepFakes: An Overview (2020, background): “Many datasets have been proposed specifically for copymove forgery detection [91], [92], [206], [95], [207], the forgery most studied in the literature.”
• MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation (2019, methodology): “The CoMoFoD dataset (2013) [33] designed for copy-move forgery detection consists of 260 forged image sets.”
• BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization (2018, methodology): “Detailed attack descriptions and settings can be found in [31].”