COVERAGE — A novel database for copy-move forgery detection
By Bihan Wen, Ye Zhu, Subramanian Ramanathan, Tian-Tsong Ng, Xuanjing Shen, Stefan Winkler. International Conference on Information Photonics, 2016
439 citations recorded by the source corpus.
Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/b8ca108a81a859da4df68ff5a996da2e347e0f9c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
- Wael AbdAlmageed
- Ramkant Nevatia
- Xuefeng Hu
- Syomantak Chaudhuri
- Zhihan Zhang
- Zhenye Jiang
- Zhenheng Yang
- Premkumar Natarajan
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Ming-Hsuan Yang
- Chen Change Loy
- Xiaogang Wang
- Yi Yang
- Bernt Schiele
- Larry S. Davis
In Media Technology
- Wenhan Yang
- Jiaying Liu
- Wael AbdAlmageed
- Ramkant Nevatia
- Xuefeng Hu
- Syomantak Chaudhuri
- Zhihan Zhang
- Zhenye Jiang
Citations in the corpus
- SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization (2020, methodology): “Manipulation Detection and Localization: Most of existing methods detect and localization a specific type of manipulation only, such as copy-move[7, 27, 33], removal[44], enhancement[5, 6], and splicing[8, 16, 18, 29].”
- ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (2019, methodology): “However, splicing [19, 44, 28], copy-move [18, 43, 37, 46, 45], removal [58], and enhancement [9, 10, 17] are the four that have been studied the most.”
- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, background): “, dehazing [6–9], deraining [10– 19], illumination enhancement [20–25], image/video compression [26–28], compression artifact removal [29, 30], and copymove forgery detection [31], only target human vision and most of image/video understanding and analytics methods, e.”
- Learning Rich Features for Image Manipulation Detection (2018, methodology): “The images are selected from the COVER, Columbia and CASIA 1.0.”
- MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection (2021, methodology): “All models TABLE 7 Overall Performance Measured by Com-F1, the Harmonic Mean of Pixel-Level F1 and Image-Level F1, on Five Test Sets Method Columbia CASIAv1+ COVER DEF-12k IMD MEAN H-LSTM 3.8 0.0 0.0 0.4 0.0 0.8 ManrTra-Net 0.0 0.0 0.0 0.0 0.0 0.0 CR-CNN 41.3 30.3 18.1 19.8 23.7 26.6 GSR-Net 4.2 0.0”