Passive digital image authentication algorithm based on Tchebichef moment invariants

Li, Mei; Gu, Zongyun; Kan, Junling

doi:10.1117/12.892167

Cited by 1 publication

(3 citation statements)

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“…On one hand, we have shown that our 1-D SDTMs algorithm leads to a better performance in audio signal than the algorithm reported by Xiao et al [33]. On the other hand, for image duplicated regions detection, our solution based on 2-D SDTMs is superior to the algorithm based on Zernike moment features and kd-tree technique reported in [12] and the algorithm proposed by Li et al [35] where the Tchebichef moments of every block are used in combination with a lexicographic technique.…”

Section: Discussionmentioning

confidence: 55%

“…The first one, which is named "Algorithm 1" hereafter, is the algorithm reported in [12], combining the Zernike moment features with a kd-tree matching. The second one, called "Algorithm 2", also based on Tchebichef moments, was proposed by Li et al [35] where the moments of every block are computed using the direct algorithm coupled with a lexicographic sorting for matching similar feature vectors.…”

Section: B) Experimental Resultsmentioning

confidence: 99%

“…2). Columns are successively the detected results using Algorithm 1 [12], Algorithm 2 [35] as well as our algorithm.…”

Section: B) Experimental Resultsmentioning

confidence: 99%

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Fast Computation of Sliding Discrete Tchebichef Moments and Its Application in Duplicated Regions Detection

Chen

Coatrieux

et al. 2015

IEEE Trans. Signal Process.

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International audienceComputational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments

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Section: Discussionmentioning

confidence: 55%

Section: B) Experimental Resultsmentioning

confidence: 99%