A natural image model approach to splicing detection

Document Type

Conference Proceeding

Publication Date

12-1-2007

Abstract

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include moments of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection. Copyright 2007 ACM.

Identifier

38849209052 (Scopus)

ISBN

[9781595938572]

Publication Title

Mm and Sec 07 Proceedings of the Multimedia and Security Workshop 2007

External Full Text Location

https://doi.org/10.1145/1288869.1288878

First Page

51

Last Page

62

This document is currently not available here.

Share

COinS