DocumentCode :
638904
Title :
Fogery image splicing detection by abnormal prediction features
Author :
Jun Hou ; Haojie Shi ; Yan Cheng ; Ran Li
Author_Institution :
Shanghai Key Lab. of Modern Opt. Syst., Univ. of Shanghai for Sci. & Technol., Shanghai, China
fYear :
2013
fDate :
4-7 Aug. 2013
Firstpage :
1394
Lastpage :
1398
Abstract :
The paper proposes an algorithm to expose photographic manipulation. Splicing photographic merges two or more parts from different photos into one composite. A well-tampered one may not be perceptible by eyes. However, even a good forgery can leave some subtle traces caused by forgery. The proposal algorithm firstly segments photo into several parts under perceptual grouping criterion, minimizing the disassociation between parts and maximizing combination within part with normalized cut algorithm. Then conduct the mean and standard variance features of inharmonic points, as well as 14 Haralick features, then fed them to a support vector machine(SVM) classifier. The test experiments show that the proposal method is effective in exposing large-size splicing photographic.
Keywords :
feature extraction; image classification; image reconstruction; image segmentation; support vector machines; Haralick features; SVM; abnormal prediction features; forgery image splicing detection; inharmonic point features; large-size splicing photographic; normalized cut algorithm; perceptual grouping criterion; photo segmentation; photographic manipulation; photographic merge splicing; support vector machine classifier; Feature extraction; Forgery; Image edge detection; Image segmentation; Proposals; Splicing; Support vector machines; Haralick features; SVM classifier; image forgery; normalized cut segmentation; splicing photographic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation (ICMA), 2013 IEEE International Conference on
Conference_Location :
Takamatsu
Print_ISBN :
978-1-4673-5557-5
Type :
conf
DOI :
10.1109/ICMA.2013.6618117
Filename :
6618117
Link To Document :
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