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Volume: 31 | Article ID: art00012
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Non-native Contents Detection and Localization for Online Fashion Images
  DOI :  10.2352/ISSN.2470-1173.2019.8.IMAWM-415  Published OnlineJanuary 2019
Abstract

Non-native content detection is about detecting regions of contents in an image that do not belong to the original or natural contents of the image. In the online fashion market, sellers often add non-native contents to their product images in order to emphasize the features of their products and get more views. However, from the buyer’s point of view, these excessive contents are often redundant and may interfere with the evaluation of the major contents or products in the image. In this paper, we propose two methods for detecting non-native content in online fashion images. The first one utilizes the special properties of image mosaicing and de-mosaicing where there are local correlations between pixels of an image. The second method is based on the periodic properties of interpolations which is a common process involved in the creation of forged images. Performance of the two methods are compared by testing on a dataset consisting of real images from an online fashion marketplace. The experimental results demonstrate the effectiveness of both methods.

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  Cite this article 

Litao Hu, Karthick Shankar, Zhi Li, Zhenxun Yuan, Jan Allebach, Gautam Glowala, Sathya Sundaram, Perry Lee, "Non-native Contents Detection and Localization for Online Fashion Imagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2019,  pp 415-1 - 415-8,  https://doi.org/10.2352/ISSN.2470-1173.2019.8.IMAWM-415

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