An efficient and robust method for detecting copy-move forgery

By Sevinc Bayram, Husrev Taha Sencar, Nasir D. Memon. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009

568 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fd75f3efb885cf2b4aea76958be4e868126354c0

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In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

Copy-move forgery detection and localization by means of robust clustering with J-Linkage (2013, background): “Authors in [19] proposed a different kind of feature that is based on the Fourier–Mellin transform that is invariant to small rotation and resizing of the copied regions.”
Detection of Copy-Rotate-Move Forgery Using Zernike Moments (2010, methodology): “We also compared ourmethod with several CRMdetectors: SIFT [10], FMT [7], and LPM [8].”
CASIA Image Tampering Detection Evaluation Database (2013, background): “Without a public standard database, many researchers tested their proposed tampering detection methods on limited examples[5, 6, 7, 8, 9, 12, 13].”
Information Forensics: An Overview of the First Decade (2013, methodology): “Unlike techniques that identify copy-move forgeries through block matching, SIFT-based approaches can identify duplicate image regions even if the duplicated region has been subject to scaling, rotation, an affine transform, or illumination adjustment [31], [32].”
Region Duplication Detection Using Image Feature Matching (2010, background): “Another vein of works use blocks in the log-polar coordinate system [5], [8], [38], where rotation and scaling become translation and can be detected as copy-move.”
BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization (2018, background): “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, methodology): “As for scale-invariance, research has mostly focused on variations of the Fourier-Mellin Transform [23]–[26], based on a log-polar sampling.”
Digital image forensics: a booklet for beginners (2010, background): “Dealing with the same problem, in [8] Bayram and others represent image segments in a Fourier-Mellin Transform”
An Overview on Image Forensics (2013, methodology): “[99] introduced the use of Fourier-Mellin transform (FMT) as block signature, since FMT is invariant to rotation and scaling.”
Segmentation-Based Image Copy-Move Forgery Detection Scheme (2015, methodology): “For example, the block-based methods [3], [4], [8] usually need a huge amount of time to detect an image.”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “The Fourier-Mellin transform (FMT) features, which are invariant to scale and rotation was extracted and lexicographic sorting is used to detect copy move forgery by Bayram et al. (2009).”
Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching (2015, methodology): “Bayram et al. [7] used the Fourier-Mellin Transform (FMT) to obtain features.”