LOCO-I: a low complexity, context-based, lossless image compression algorithm
DCC '96 Proceedings of the Conference on Data Compression
Detecting Digital Forgeries Using Bispectral Analysis
Detecting Digital Forgeries Using Bispectral Analysis
Signal Period Analysis Based on Hilbert-Huang Transform and Its Application to Texture Analysis
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A natural image model approach to splicing detection
Proceedings of the 9th workshop on Multimedia & security
Study of Image Splicing Detection
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Steganalysis Versus Splicing Detection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
A bibliography on blind methods for identifying image forgery
Image Communication
Digital image forensics: a booklet for beginners
Multimedia Tools and Applications
Revealing digital fakery using multiresolution decomposition and higher order statistics
Engineering Applications of Artificial Intelligence
Digital image splicing detection based on approximate run length
Pattern Recognition Letters
Markovian rake transform for digital image tampering detection
Transactions on data hiding and multimedia security VI
Digital image splicing detection based on Markov features in DCT and DWT domain
Pattern Recognition
Improved run length based detection of digital image splicing
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
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Image splicing is a commonly used technique in image tampering. This paper presents a novel approach to passive detection of image splicing. In the proposed scheme, the image splicing detection problem is tackled as a twoclass classification problem under the pattern recognition framework. Considering the high non-linearity and non-stationarity nature of image splicing operation, a recently developed Hilbert-Huang transform (HHT) is utilized to generate features for classification. Furthermore, a well established statistical natural image model based on moments of characteristic functions with wavelet decomposition is employed to distinguish the spliced images from the authentic images. We use support vector machine (SVM) as the classifier. The initial experimental results demonstrate that the proposed scheme outperforms the prior arts.