Detection of copy-move forgery using a method based on blur moment invariants.

By Babak Mahdian, Stanislav Saic. Forensic Science International, 2007

470 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/11d2536ad019f525856403cda44f6151ae9cf6bd

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In Computer Vision and Pattern Recognition

Citations in the corpus

Region Duplication Detection Using Image Feature Matching (2010, unknown): “, [4], [15], [24], [26], [28], [33], [36], [41], [48]).”
Using noise inconsistencies for blind image forensics (2009, background): “An ROI can be identified by one of the forgery detection techniques capable of localizing the tampering as well (for example [4,10,6]).”
Image forgery detection (2009, methodology): “Two computationally efficient algorithms have been developed to detect cloned image regions ( [11], [34] ; see also [23], [27], and [42] ).”
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”
Detection of Copy-Rotate-Move Forgery Using Zernike Moments (2010, methodology): “We ﬁrstly conducted our experiments with 12 TIFF images from a personal collection and [3,6].”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, background): “Most of the state-of-the-art image tamper classiﬁcation approaches utilize the frequency domain characteristics and/or statistical properties of an image [46], [56], [58], [85].”
Copy-move forgery detection and localization by means of robust clustering with J-Linkage (2013, background): “has been presented in [18] where the authors proposed a block-based representation calculated using blur invariants.”
Efficient Dense-Field Copy–Move Forgery Detection (2015, methodology): “In the literature, to improve robustness, features are typically extracted through some transforms, like DCT [14]–[16], Wavelet [17], PCA [18], SVD [19], reducing also their length, thanks to the decorrelation of coefficients.”