The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Copy–Move Forgery Detection by Matching Triangles of Keypoints
Abstract
Copy-move forgery is one of the most common types of tampering for digital images. Detection methods generally use block-matching approaches, which first divide the image into overlapping blocks and then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
A Combined Corner and Edge Detector
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Learning to Detect a Salient Object
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., & Shum, H.-Y. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.70 · Cited by 1,777
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2129512 · Cited by 1,044
Detection of Copy-Move Forgery in Digital Images
Fridrich, J. (2004) · Cited by 1,040
Farid, H. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.931079 · Cited by 925
An Evaluation of Popular Copy-Move Forgery Detection Approaches
Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2218597 · Cited by 802
Exposing Digital Forgeries by Detecting Duplicated Image Regions
Popescu, A., & Farid, H. (2004) · Cited by 641
Cited by
Learning Self-Consistency for Deepfake Detection
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383
A detailed analysis of image and video forgery detection techniques
Tyagi, S., & Yadav, D. (2023) · The Visual Computer · doi:10.1007/s00371-021-02347-4 · Cited by 109
Metadata
- Authors
- Ardizzone, E., Bruno, A., & Mazzola, G.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2015-10
- Volume
- 10
- Issue
- 10
- Pages
- 2084-2094
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W1490753124
- Cited by
- 266
- References
- 23
- FWCI
- 19.35
- Type
- journal article
- Language
- en
- Open access
- closed
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