New developments in color image tampering detection
- Resource Type
- Conference
- Authors
- Sutthiwan, Patchara; Shi, Yun-Qing; Dong, Jing; Tan, Tieniu; Ng, Tian-Tsong
- Source
- 2010 IEEE International Symposium on Circuits and Systems (ISCAS) Circuits and Systems (ISCAS), 2010 IEEE International Symposium on. :3064-3067 May, 2010
- Subject
- Components, Circuits, Devices and Systems
Color
Splicing
Forgery
Feature extraction
Testing
Discrete cosine transforms
Computer vision
Support vector machines
Boosting
Watermarking
color image tampering detection
multi-size block discrete cosine transform
moments of characteristic functions
Markov process
support vector machine
boosting feature selection
- Language
- ISSN
- 0271-4302
2158-1525
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.