Video logo removal detection based on sparse representation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11

AUTHORS

Xiao Jin, Yuting Su, Liang Zou, Chengqian Zhang, Peiguang Jing, Xuemeng Song

ABSTRACT

With the popularity of multimedia editing tools, more and more forged multimedia content appeared on the network. Thus, the legal authorities need novel techniques to distinguish copyright infringements from a large number of videos on the Internet. Since logo removal is a common editing operation during unauthorized reproduction, logo removal detection is often equivalent to copyright infringements detection to some extent. In this paper, we proposed a video forensics framework for logo removal detection. Our framework mainly contains two stages: the removal traces detection and the removal region location. In the first stage, we use sparse representation to show the difference between the tampered areas and the original areas in sparsity. In the second stage, spatial priors and temporal correlations are used to refine the location of the removal regions. Finally, a spatiotemporal suspected region can obviously show the edited regions. The proposed method is validated on our video logo removal dataset by extensive experiments, showing promising results. More... »

PAGES

29303-29322

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-018-5959-8

DOI

http://dx.doi.org/10.1007/s11042-018-5959-8

DIMENSIONS

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47 schema:description With the popularity of multimedia editing tools, more and more forged multimedia content appeared on the network. Thus, the legal authorities need novel techniques to distinguish copyright infringements from a large number of videos on the Internet. Since logo removal is a common editing operation during unauthorized reproduction, logo removal detection is often equivalent to copyright infringements detection to some extent. In this paper, we proposed a video forensics framework for logo removal detection. Our framework mainly contains two stages: the removal traces detection and the removal region location. In the first stage, we use sparse representation to show the difference between the tampered areas and the original areas in sparsity. In the second stage, spatial priors and temporal correlations are used to refine the location of the removal regions. Finally, a spatiotemporal suspected region can obviously show the edited regions. The proposed method is validated on our video logo removal dataset by extensive experiments, showing promising results.
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