Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency
By Yu-Feng Hsu, Shih-Fu Chang. IEEE International Conference on Multimedia and Expo, 2006
454 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/f429b695a0d24593400e2b73dace4908320fbf68
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In Computer Vision and Pattern Recognition
- Luisa Verdoliva
- Davide Cozzolino
- Matthew Christopher Stamm
- Luc Van Gool
- C.-C. Jay Kuo
- Vijay Harishchandra Mankar
- Gajanan K. Birajdar
- Zhouchen Lin
Citations in the corpus
- Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “Popular targets of such analyses are lens aberration [6], [7], [8], camera response function [9], [10], [11], color filter array (CFA) [12], [13], [14] or JPEG artifacts [15], [16], [17], [18].”
- Information Forensics: An Overview of the First Decade (2013, background): “Recently, an alternate means of identifying copy-move forgeries using scale-invariant feature transform (SIFT) points has been proposed [29].”
- The 'Dresden Image Database' for benchmarking digital image forensics (2010, background): “A commendable exception is the image splicing database by [7].”
- Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis (2009, background): “2(c)) refers to a block in the tampered region or along the synthesis edge and an unchanged block is a block in the unchanged region.”
- RAISE: a raw images dataset for digital image forensics (2015, methodology): “The Columbia Uncompressed Image Splicing Detection Evaluation Dataset [12] provides 183 authentic and niques, thus providing the predefined query images with the corresponding model images that should be retrieved (released as ground truth).”
- Digital image forgery detection using passive techniques: A survey (2013, methodology): “Kharrazi et al. (2004) developed a method of identifying the source camera of a digital image using the color processing/transformation and a set of IQM.”
- Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) (2017, background): “0, Columbia Uncompressed [16], Carvalho [11], and the DARPA/NIST Nim-ble Challenge 2016 Science (SCI) datasets 1 .”