Detection of Copy-Rotate-Move Forgery Using Zernike Moments
By Seung-Jin Ryu, Min-Jeong Lee, Heung-Kyu Lee. Information Hiding, 2010
373 citations recorded by the source corpus.
Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/b3ee279e58560970bf08e136b19b67e6f0bd7822
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Computer Vision and Pattern Recognition
- Premkumar Natarajan
- Yue Wu
- Wael Abd-Almageed
- Irene Amerini
- Roberto Caldelli
- Lamberto Ballan
- Luca Del Tongo
- Giuseppe Serra
In Media Technology
- Premkumar Natarajan
- Yue Wu
- Wael Abd-Almageed
- Irene Amerini
- Roberto Caldelli
- Giuseppe Serra
- A. del Bimbo
- Luisa Verdoliva
Citations in the corpus
- Copy-move forgery detection and localization by means of robust clustering with J-Linkage (2013, methodology): “[23] exploited the Zernike moments as features since their magnitude is algebraically invariant to rotation transformation.”
- BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization (2018, methodology): “Copy-move detection frameworks can be broadly classified, based on the underlying feature extraction and subsequent matching schemes, into three main categories: patch/block-based methods such as chroma features [4,9], PCA feature [14], Zernike moments [26], blur moments [20], DCT [21]; keypoint-bas”
- Efficient Dense-Field Copy–Move Forgery Detection (2015, neutral): “4 on a real-world example.”