Abstract
Use of sophisticated image editing tools and computer graphics makes easy to edit, transform, or eliminate the significant features of an image without leaving any prominent proof of tampering. One of the most commonly used tampering techniques is image splicing. In image splicing, a portion of image is cut and paste it on the same image or different image to generate a new tampered image, which is hardly noticeable by naked eyes. In the proposed method, enhanced Markov model is applied in the block discrete cosine transform (BDCT) domain as well as in discrete Meyer wavelet transform (DMWT) domain. To classify the spliced image from an authentic image, the cross-domain features play the role of final discriminative features for support vector machine (SVM) classifier. The performance of the proposed method through experiments is estimated on the publicly available dataset (Columbia dataset) for image splicing. The experimental results show that the proposed method performs better than some of the existing state of the art.
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Kumar, A., Prakash, C.S., Maheshkar, S., Maheshkar, V. (2019). Markov Feature Extraction Using Enhanced Threshold Method for Image Splicing Forgery Detection. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-8971-8_2
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DOI: https://doi.org/10.1007/978-981-10-8971-8_2
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