Image Splicing Detection Based on Markov Features in QDCT Domain

• Published in: Intelligent Computing Theories and Methodologies Vol. 9226; pp. 170 - 176
• Main Authors: Li, Ce, Ma, Qiang, Xiao, Limei, Li, Ming, Zhang, Aihua
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Color image forgery detection; Image processing; Image-splicing detection; Markov model; Pattern recognition; QDCT
• ISBN: 9783319221854; 331922185X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-22186-1_17

Image splicing is very common and fundamental in image tampering. Therefore, image splicing detection has attracted more and more attention recently in digital forensics. Gray images are used directly, or color images are converted to gray images before processing in previous image splicing detection algorithms. However, most natural images are color images. In order to make use of the color information in images, a classification algorithm is put forward which can use color images directly. In this paper, an algorithm based on Markov in Quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection. The support vector machine (SVM) is exploited to classify the authentic and spliced images. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve high classification accuracy.