Abstract
In this paper, an efficient framework for passive-blind color image tampering detection is presented. Statistical features are extracted from a given test image and a set of 2-D arrays derived by applying multi-size block discrete cosine transform to the given test image. Image features are extracted from Cr channel, a chroma channel in YCbCr color space, because of its observed sensitivity to color image tampering. A support vector machine is employed to evaluate the effectiveness of image features over a color image dataset recently established for tampering detection. Boosting feature selection is applied to having feature dimensionality reduced so as to make detection accuracy generalizable and computational complexity decreased. Experimental results have demonstrated that the proposed framework applied to the aforementioned dataset outperforms the state of the arts by distinct margins.
Original language | English (US) |
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Title of host publication | ISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems |
Subtitle of host publication | Nano-Bio Circuit Fabrics and Systems |
Pages | 3064-3067 |
Number of pages | 4 |
DOIs | |
State | Published - Aug 31 2010 |
Event | 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010 - Paris, France Duration: May 30 2010 → Jun 2 2010 |
Other
Other | 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010 |
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Country/Territory | France |
City | Paris |
Period | 5/30/10 → 6/2/10 |
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Electrical and Electronic Engineering