Robust Detection of Region-Duplication Forgery in Digital Image

By Weiqi Luo, Jiwu Huang, Guoping Qiu. International Conference on Pattern Recognition, 2006

328 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/635ffbfb91ac6ea4c7593bf2ae77a5b9be09e1fb

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In Computer Vision and Pattern Recognition

Citations in the corpus

An Evaluation of Digital Image Forgery Detection Approaches (2017, background): “[16] proposed a strong identification of duplicated region in digital images in 2006.”
Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm (2008, methodology): “Figure 3 is another forged gray image example used by [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): “A different kind of features are used in [16], in fact the authors choose the averages of red, green and blue components with other four features, computed on overlapping blocks, obtained by calculating the energy distribution of luminance along four different directions.”
Detection of Copy-Rotate-Move Forgery Using Zernike Moments (2010, background): “measured block characteristics vector from each block [4,5].”
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.”
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].”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Several surveys have been published on image forgery detection: Rocha et al. (2011), Farid (2009a), Mahdian and Saic (2010), Lanh et al. (2007a), Luo et al. (2007a), Mahdian and Saic (2008a), Ng et al. (2006), Sencar and Memon (2008), Zhang et al. (2008a), Bayram et al. (2008a) and Redi et al.…”
Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching (2015, methodology): “main categories: block-based algorithms [1-13] and feature key point-based algorithms [14-19].”