Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm

By Hailing Huang, Weiqiang Guo, Yu Zhang. IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008

449 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3badba44db26d1df120f702b95fe80daca5082d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Vijay Harishchandra Mankar
2. Gajanan K. Birajdar
3. Matthew Christopher Stamm
4. Xiaolong Li
5. Luisa Verdoliva
6. Bin Yang
7. Min Wu
8. Xingming Sun

In Media Technology

1. Gajanan K. Birajdar
2. Vijay Harishchandra Mankar
3. Matthew Christopher Stamm
4. Min Wu
5. Xiaolong Li
6. Xingming Sun
7. Jian Li
8. Bin Yang

Citations in the corpus

• Digital image forgery detection using passive techniques: A survey (2013, background): “Scale invariant features transform (SIFT) features which are stable with respect to changes in illumination, rotation and scaling applied by Huang et al. (2008) to detect the cloned regions in the image.”
• Detection of Copy-Rotate-Move Forgery Using Zernike Moments (2010, background): “There are some approaches that extracted interest points on the whole image by scale-invariant feature transform (SIFT) [9,10,11].”
• Region Duplication Detection Using Image Feature Matching (2010, methodology): “But similar to [21], it does not provide the exact extent and location of the detected duplicated region, but only displays the matched keypoints.”
• 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 forensics: a booklet for beginners (2010, methodology): “[45] exploits SIFT features to obtain robust tampering detection even under geometric transforms.”
• Segmentation-Based Image Copy-Move Forgery Detection Scheme (2015, background): “The second class of algorithms detects the CMF through observing the keypoints in the image [10]–[15].”
• An Evaluation of Digital Image Forgery Detection Approaches (2017, methodology): “Huang et al. [9] proposed copy-move forgery detection in digital images using SIFT algorithm in 2009.”